Continuous control with deep reinforcement learning

Abstract

We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. Using the same learning algorithm, network architecture and hyper-parameters, our algorithm robustly solves more than 20 simulated physics tasks, including classic problems such as cartpole swing-up, dexterous manipulation, legged locomotion and car driving. Our algorithm is able to find policies whose performance is competitive with those found by a planning algorithm with full access to the dynamics of the domain and its derivatives. We further demonstrate that for many of the tasks the algorithm can learn policies end-to-end: directly from raw pixel inputs.

10 pages + supplementary

References in corpus (6)

• Adam: A Method for Stochastic Optimization
• End-to-End Training of Deep Visuomotor Policies
• Learning Continuous Control Policies by Stochastic Value Gradients
• Memory-based control with recurrent neural networks
• Gradient Estimation Using Stochastic Computation Graphs
• Compatible Value Gradients for Reinforcement Learning of Continuous Deep Policies

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• Bipedal Walking Robot using Deep Deterministic Policy Gradient
• Exploration versus exploitation in reinforcement learning: a stochastic control approach
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• Leveraging the Capabilities of Connected and Autonomous Vehicles and Multi-Agent Reinforcement Learning to Mitigate Highway Bottleneck Congestion
• Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones
• Formulation and validation of a car-following model based on deep reinforcement learning
• VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation
• Reinforcement Learning on Variable Impedance Controller for High-Precision Robotic Assembly
• Solving NP-Hard Problems on Graphs with Extended AlphaGo Zero
• A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions
• Distributed multi-agent target search and tracking with Gaussian process and reinforcement learning
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• Model-Based Offline Planning
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• Reinforcement Learning for Photonic Component Design
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• Deep Reinforcement Learning for Dynamic Multichannel Access in Wireless Networks
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• Learning a Decentralized Multi-arm Motion Planner
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• PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals
• SafeCritic: Collision-Aware Trajectory Prediction
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• Meta-SAC: Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient
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• Quantum reinforcement learning in continuous action space
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• Reinforcement Learning for Shared Autonomy Drone Landings
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• Regularized Softmax Deep Multi-Agent $Q$-Learning
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• Learning to Combat Compounding-Error in Model-Based Reinforcement Learning
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• Dynamic Programming Principles for Mean-Field Controls with Learning
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• Hamilton-Jacobi Deep Q-Learning for Deterministic Continuous-Time Systems with Lipschitz Continuous Controls
• From self-tuning regulators to reinforcement learning and back again
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• Robust Deep Reinforcement Learning through Adversarial Loss
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• Generation and storage of spin squeezing via learning-assisted optimal control
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• Learning Nash Equilibrium for General-Sum Markov Games from Batch Data
• Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing Systems
• Reinforcement Learning with Perturbed Rewards
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• Modern Deep Reinforcement Learning Algorithms
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• Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting
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• Using Simulation Optimization to Improve Zero-shot Policy Transfer of Quadrotors
• Reinforced Epidemic Control: Saving Both Lives and Economy
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• Curiosity-Driven Multi-Criteria Hindsight Experience Replay
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• Contrastive Explanations for Reinforcement Learning via Embedded Self Predictions
• Optimistic Policy Optimization with Bandit Feedback
• Ablation of a Robot's Brain: Neural Networks Under a Knife
• Challenges of Applying Deep Reinforcement Learning in Dynamic Dispatching
• Automatic Inverse Treatment Planning for Gamma Knife Radiosurgery via Deep Reinforcement Learning
• Automated vehicle's behavior decision making using deep reinforcement learning and high-fidelity simulation environment
• Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learning: A Field Experiment
• An Open-Source Framework for Adaptive Traffic Signal Control
• ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots
• Case Study: Verifying the Safety of an Autonomous Racing Car with a Neural Network Controller
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• Self-Adversarial Learning with Comparative Discrimination for Text Generation
• Safe-visor Architecture for Sandboxing (AI-based) Unverified Controllers in Stochastic Cyber-Physical Systems
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• MADE: Exploration via Maximizing Deviation from Explored Regions
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• Conditional Driving from Natural Language Instructions
• Robotic Surgery With Lean Reinforcement Learning
• Learning Agent Communication under Limited Bandwidth by Message Pruning
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• An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay
• Multi-task Batch Reinforcement Learning with Metric Learning
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• Causality and Batch Reinforcement Learning: Complementary Approaches To Planning In Unknown Domains
• Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments
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• Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data
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• Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning
• Continual Reinforcement Learning with Multi-Timescale Replay
• Model-based Lookahead Reinforcement Learning
• Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication
• Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile Networks
• Policy Gradient in Partially Observable Environments: Approximation and Convergence
• Optimizing Mixed Autonomy Traffic Flow With Decentralized Autonomous Vehicles and Multi-Agent RL
• Uncertainty-aware Model-based Policy Optimization
• Symbolic Regression Methods for Reinforcement Learning
• Offline Reinforcement Learning for Autonomous Driving with Safety and Exploration Enhancement
• Design Space of Behaviour Planning for Autonomous Driving
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• Large-scale traffic signal control using machine learning: some traffic flow considerations
• Optimization Landscape of Gradient Descent for Discrete-time Static Output Feedback
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• Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking
• Neural Stochastic Dual Dynamic Programming
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• The Emergence of Wireless MAC Protocols with Multi-Agent Reinforcement Learning
• Hardware as Policy: Mechanical and Computational Co-Optimization using Deep Reinforcement Learning
• ADVERSARIALuscator: An Adversarial-DRL Based Obfuscator and Metamorphic Malware SwarmGenerator
• Multi-Agent Actor-Critic with Generative Cooperative Policy Network
• Meta Reinforcement Learning with Task Embedding and Shared Policy
• Risk Adversarial Learning System for Connected and Autonomous Vehicle Charging
• Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads
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• Affordance-based Reinforcement Learning for Urban Driving
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• Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles
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• Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration
• Guided Policy Search Model-based Reinforcement Learning for Urban Autonomous Driving
• Scalable Voltage Control using Structure-Driven Hierarchical Deep Reinforcement Learning
• Geometric Entropic Exploration
• Context-Aware Safe Reinforcement Learning for Non-Stationary Environments
• Deep Reinforcement Learning for Tropical Air Free-Cooled Data Center Control
• Thief, Beware of What Get You There: Towards Understanding Model Extraction Attack
• ACtuAL: Actor-Critic Under Adversarial Learning
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• Evolving Inborn Knowledge For Fast Adaptation in Dynamic POMDP Problems
• A Deep Reinforcement Learning Approach for Traffic Signal Control Optimization
• Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing
• Reinforcement Learning Your Way: Agent Characterization through Policy Regularization
• Learning Deep Mean Field Games for Modeling Large Population Behavior
• The Effect of Multi-step Methods on Overestimation in Deep Reinforcement Learning
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• Adversarial Reinforcement Learning for Observer Design in Autonomous Systems under Cyber Attacks
• Multi-Agent Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing
• Medical Dead-ends and Learning to Identify High-risk States and Treatments
• Trajectory Design for UAV-Based Internet-of-Things Data Collection: A Deep Reinforcement Learning Approach
• Offline Meta-Reinforcement Learning with Online Self-Supervision
• Decentralized Q-Learning in Zero-sum Markov Games
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• Efficient time stepping for numerical integration using reinforcement learning
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• Storchastic: A Framework for General Stochastic Automatic Differentiation
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• Deep Reinforcement Learning for Long-Short Portfolio Optimization
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• Designing high-fidelity multi-qubit gates for semiconductor quantum dots through deep reinforcement learning
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• Automatic Neural Network Compression by Sparsity-Quantization Joint Learning: A Constrained Optimization-based Approach
• Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FEL
• POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems
• Indoor Point-to-Point Navigation with Deep Reinforcement Learning and Ultra-wideband
• Teach Biped Robots to Walk via Gait Principles and Reinforcement Learning with Adversarial Critics
• Real-time Artificial Intelligence for Accelerator Control: A Study at the Fermilab Booster
• Meta Automatic Curriculum Learning
• Primal-dual Learning for the Model-free Risk-constrained Linear Quadratic Regulator
• Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning
• Balance Between Efficient and Effective Learning: Dense2Sparse Reward Shaping for Robot Manipulation with Environment Uncertainty
• Trust Region Value Optimization using Kalman Filtering
• Deep Learning of Koopman Representation for Control
• Relative Importance Sampling for off-Policy Actor-Critic in Deep Reinforcement Learning
• Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation
• Action-Sufficient State Representation Learning for Control with Structural Constraints
• Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks
• Off-Policy Actor-Critic in an Ensemble: Achieving Maximum General Entropy and Effective Environment Exploration in Deep Reinforcement Learning
• Hashing over Predicted Future Frames for Informed Exploration of Deep Reinforcement Learning
• Generalized Decision Transformer for Offline Hindsight Information Matching
• GRAC: Self-Guided and Self-Regularized Actor-Critic
• Catalyst.RL: A Distributed Framework for Reproducible RL Research
• Adversarial Skill Chaining for Long-Horizon Robot Manipulation via Terminal State Regularization
• FIXAR: A Fixed-Point Deep Reinforcement Learning Platform with Quantization-Aware Training and Adaptive Parallelism
• Symbolic Regression via Neural-Guided Genetic Programming Population Seeding
• Reinforcement Learning Evaluation and Solution for the Feedback Capacity of the Ising Channel with Large Alphabet
• How Much Do Unstated Problem Constraints Limit Deep Robotic Reinforcement Learning?
• Soft Expert Reward Learning for Vision-and-Language Navigation
• Imitation Learning for Human Pose Prediction
• Zeroth-order Deterministic Policy Gradient
• Benchmarking Inverse Optimization Algorithms for Materials Design
• Value-Decomposition Multi-Agent Actor-Critics
• Implicit Distributional Reinforcement Learning
• Is the Policy Gradient a Gradient?
• Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment
• Non-local Policy Optimization via Diversity-regularized Collaborative Exploration
• Mutual Information Based Knowledge Transfer Under State-Action Dimension Mismatch
• Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments
• Generalized Off-Policy Actor-Critic
• Variational Autoencoders for Opponent Modeling in Multi-Agent Systems
• Deep Emotion: A Computational Model of Emotion Using Deep Neural Networks
• Towards Safe Control of Continuum Manipulator Using Shielded Multiagent Reinforcement Learning
• Safe Learning MPC with Limited Model Knowledge and Data
• Mean-Variance Policy Iteration for Risk-Averse Reinforcement Learning
• Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning
• Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control
• Deep Learning in Robotics: A Review of Recent Research
• Reinforcement Learning for Quantitative Trading
• Dynamic Control of Stochastic Evolution: A Deep Reinforcement Learning Approach to Adaptively Targeting Emergent Drug Resistance
• Robotic Table Tennis with Model-Free Reinforcement Learning
• AI based Algorithms of Path Planning, Navigation and Control for Mobile Ground Robots and UAVs
• Developing a Simple Model for Sand-Tool Interaction and Autonomously Shaping Sand
• MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding
• A Visual Communication Map for Multi-Agent Deep Reinforcement Learning
• Incremental Reinforcement Learning --- a New Continuous Reinforcement Learning Frame Based on Stochastic Differential Equation methods
• Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning
• Steady State Analysis of Episodic Reinforcement Learning
• Constrained Model-Free Reinforcement Learning for Process Optimization
• Efficient Deep Reinforcement Learning via Adaptive Policy Transfer
• Deep Reinforcement Learning with Feedback-based Exploration
• Nonparametric Stochastic Compositional Gradient Descent for Q-Learning in Continuous Markov Decision Problems
• Self-supervised Graph Representation Learning via Bootstrapping
• StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer Learning
• MRAC-RL: A Framework for On-Line Policy Adaptation Under Parametric Model Uncertainty
• Cache-Enabled Dynamic Rate Allocation via Deep Self-Transfer Reinforcement Learning
• Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation
• Learning to Engage with Interactive Systems: A Field Study on Deep Reinforcement Learning in a Public Museum
• Only Relevant Information Matters: Filtering Out Noisy Samples to Boost RL
• DiGrad: Multi-Task Reinforcement Learning with Shared Actions
• Anti-Jerk On-Ramp Merging Using Deep Reinforcement Learning
• Tailored Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud System
• Reinforcement Learning via Parametric Cost Function Approximation for Multistage Stochastic Programming
• Cooperative Highway Work Zone Merge Control based on Reinforcement Learning in A Connected and Automated Environment
• Wide Area Measurement System-based Low Frequency Oscillation Damping Control through Reinforcement Learning
• Reinforcement Learning with Probabilistically Complete Exploration
• Estimating Q(s,s') with Deep Deterministic Dynamics Gradients
• Deep Reinforcement Learning for Producing Furniture Layout in Indoor Scenes
• UDO: Universal Database Optimization using Reinforcement Learning
• Episodic Linear Quadratic Regulators with Low-rank Transitions
• Iterative Amortized Policy Optimization
• Deep reinforcement learning for the control of conjugate heat transfer with application to workpiece cooling
• Few Shot System Identification for Reinforcement Learning
• Coordination in Adversarial Sequential Team Games via Multi-Agent Deep Reinforcement Learning
• When Do Drivers Concentrate? Attention-based Driver Behavior Modeling With Deep Reinforcement Learning
• UAV-to-Device Underlay Communications: Age of Information Minimization by Multi-agent Deep Reinforcement Learning
• An Off-policy Policy Gradient Theorem Using Emphatic Weightings
• Reward Tweaking: Maximizing the Total Reward While Planning for Short Horizons
• TauRieL: Targeting Traveling Salesman Problem with a deep reinforcement learning inspired architecture
• Curious Exploration and Return-based Memory Restoration for Deep Reinforcement Learning
• Skill Transfer in Deep Reinforcement Learning under Morphological Heterogeneity
• Policy Analysis using Synthetic Controls in Continuous-Time
• Obstacle Avoidance and Navigation Utilizing Reinforcement Learning with Reward Shaping
• The Adversarial Resilience Learning Architecture for AI-based Modelling, Exploration, and Operation of Complex Cyber-Physical Systems
• Multi-Agent Deep Reinforcement Learning based Spectrum Allocation for D2D Underlay Communications
• Evaluating the progress of Deep Reinforcement Learning in the real world: aligning domain-agnostic and domain-specific research
• Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligence
• Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning
• Multiple-objective Reinforcement Learning for Inverse Design and Identification
• Learning Density Distribution of Reachable States for Autonomous Systems
• A User's Guide to Calibrating Robotics Simulators
• Tutoring Reinforcement Learning via Feedback Control
• Soft Policy Gradient Method for Maximum Entropy Deep Reinforcement Learning
• Continuous Homeostatic Reinforcement Learning for Self-Regulated Autonomous Agents
• Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning
• Learning Navigation Behaviors End-to-End with AutoRL
• Variance-Reduced Off-Policy Memory-Efficient Policy Search
• Intelligent Coordination among Multiple Traffic Intersections Using Multi-Agent Reinforcement Learning
• Dynamic Coded Caching in Wireless Networks Using Multi-Agent Reinforcement Learning
• Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Coordination by Multi-Critic Policy Gradient Optimization
• Dampen the Stop-and-Go Traffic with Connected and Automated Vehicles -- A Deep Reinforcement Learning Approach
• Theory-based Causal Transfer: Integrating Instance-level Induction and Abstract-level Structure Learning
• Deep Stock Trading: A Hierarchical Reinforcement Learning Framework for Portfolio Optimization and Order Execution
• Smart Train Operation Algorithms based on Expert Knowledge and Reinforcement Learning
• Synthesizing Chemical Plant Operation Procedures using Knowledge, Dynamic Simulation and Deep Reinforcement Learning
• Learning Multi-agent Skills for Tabular Reinforcement Learning using Factor Graphs
• On the Sample Complexity of Reinforcement Learning with Policy Space Generalization
• Deep Learning with Experience Ranking Convolutional Neural Network for Robot Manipulator
• Self-organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networks
• Deep Reinforcement Learning for Multi-Agent Power Control in Heterogeneous Networks
• Analysing Factorizations of Action-Value Networks for Cooperative Multi-Agent Reinforcement Learning
• Domain-Robust Visual Imitation Learning with Mutual Information Constraints
• Cooperative Internet of UAVs: Distributed Trajectory Design by Multi-agent Deep Reinforcement Learning
• TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL
• Data-driven Outer-Loop Control Using Deep Reinforcement Learning for Trajectory Tracking
• Stealing Deep Reinforcement Learning Models for Fun and Profit
• Combining Reinforcement Learning with Model Predictive Control for On-Ramp Merging
• Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning
• Enhancing a Neurocognitive Shared Visuomotor Model for Object Identification, Localization, and Grasping With Learning From Auxiliary Tasks
• Linear Representation Meta-Reinforcement Learning for Instant Adaptation
• Learning Dynamics Models for Model Predictive Agents
• Low-Bandwidth Communication Emerges Naturally in Multi-Agent Learning Systems
• Deep Reinforcement Learning for Scheduling in Cellular Networks
• Regularized Behavior Value Estimation
• AW-Opt: Learning Robotic Skills with Imitation and Reinforcement at Scale
• Qgraph-bounded Q-learning: Stabilizing Model-Free Off-Policy Deep Reinforcement Learning
• Continuous-Time Model-Based Reinforcement Learning
• Learning Cross-Domain Correspondence for Control with Dynamics Cycle-Consistency
• Sequoia: A Software Framework to Unify Continual Learning Research
• Conformal Bootstrap with Reinforcement Learning
• A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media
• Caching Transient Content for IoT Sensing: Multi-Agent Soft Actor-Critic
• Multi-Agent Path Planning based on MPC and DDPG
• Flappy Hummingbird: An Open Source Dynamic Simulation of Flapping Wing Robots and Animals
• Solving Challenging Dexterous Manipulation Tasks With Trajectory Optimisation and Reinforcement Learning
• Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection
• Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification
• AMBER: Adaptive Multi-Batch Experience Replay for Continuous Action Control
• EdgeSlice: Slicing Wireless Edge Computing Network with Decentralized Deep Reinforcement Learning
• Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification
• Towards Optimal District Heating Temperature Control in China with Deep Reinforcement Learning
• Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles
• Reinforcement Learning for Joint Optimization of Multiple Rewards
• Imitation Learning from Pixel-Level Demonstrations by HashReward
• A Learning Framework for High Precision Industrial Assembly
• Accelerating Safe Reinforcement Learning with Constraint-mismatched Policies
• Reciprocal Collision Avoidance for General Nonlinear Agents using Reinforcement Learning
• Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning
• Implicit Generative Modeling for Efficient Exploration
• TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems
• Adversarial Intrinsic Motivation for Reinforcement Learning
• DOB-Net: Actively Rejecting Unknown Excessive Time-Varying Disturbances
• Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning
• Efficient Neural Interaction Function Search for Collaborative Filtering
• Continuous Control for Searching and Planning with a Learned Model
• Learnable Strategies for Bilateral Agent Negotiation over Multiple Issues
• Multi-Agent Deep Reinforcement Learning with Adaptive Policies
• Towards Return Parity in Markov Decision Processes
• DRAS-CQSim: A Reinforcement Learning based Framework for HPC Cluster Scheduling
• Communication-Efficient Policy Gradient Methods for Distributed Reinforcement Learning
• Competitive Experience Replay
• Correcting Experience Replay for Multi-Agent Communication
• A Crash Course on Reinforcement Learning
• Robust Multi-Agent Reinforcement Learning with Social Empowerment for Coordination and Communication
• CARL: A Benchmark for Contextual and Adaptive Reinforcement Learning
• Reward-estimation variance elimination in sequential decision processes
• Learning Value Functions in Deep Policy Gradients using Residual Variance
• Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discovery
• Towards Automatic Actor-Critic Solutions to Continuous Control
• PBCS : Efficient Exploration and Exploitation Using a Synergy between Reinforcement Learning and Motion Planning
• Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data
• My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control
• Learning Image-Conditioned Dynamics Models for Control of Under-actuated Legged Millirobots
• Adaptive Synthetic Characters for Military Training
• Q-Networks for Binary Vector Actions
• Belief-Grounded Networks for Accelerated Robot Learning under Partial Observability
• Continuous Deep Hierarchical Reinforcement Learning for Ground-Air Swarm Shepherding
• Nested Reinforcement Learning Based Control for Protective Relays in Power Distribution Systems
• Reinforcement Learning based Proactive Control for Transmission Grid Resilience to Wildfire
• IR-VIC: Unsupervised Discovery of Sub-goals for Transfer in RL
• CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving
• Diversity-based Trajectory and Goal Selection with Hindsight Experience Replay
• Reinforcement Learning Meets Hybrid Zero Dynamics: A Case Study for RABBIT
• HyAR: Addressing Discrete-Continuous Action Reinforcement Learning via Hybrid Action Representation
• Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning
• Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey
• Game Theory and Machine Learning in UAVs-Assisted Wireless Communication Networks: A Survey
• Learning to Solve a Rubik's Cube with a Dexterous Hand
• Vision-Dialog Navigation by Exploring Cross-modal Memory
• Proximal Policy Gradient: PPO with Policy Gradient
• Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning
• Backprop-Q: Generalized Backpropagation for Stochastic Computation Graphs
• Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path
• Conservative Data Sharing for Multi-Task Offline Reinforcement Learning
• Experience Augmentation: Boosting and Accelerating Off-Policy Multi-Agent Reinforcement Learning
• Offline Decentralized Multi-Agent Reinforcement Learning
• Hierarchically Decoupled Imitation for Morphological Transfer
• Recruitment-imitation Mechanism for Evolutionary Reinforcement Learning
• On Instrumental Variable Regression for Deep Offline Policy Evaluation
• Composite Q-learning: Multi-scale Q-function Decomposition and Separable Optimization
• Text Generation Based on Generative Adversarial Nets with Latent Variable
• On the Search for Feedback in Reinforcement Learning
• Multiple Access in Dynamic Cell-Free Networks: Outage Performance and Deep Reinforcement Learning-Based Design
• Review, Analysis and Design of a Comprehensive Deep Reinforcement Learning Framework
• AutoEG: Automated Experience Grafting for Off-Policy Deep Reinforcement Learning
• From Credit Assignment to Entropy Regularization: Two New Algorithms for Neural Sequence Prediction
• ANS: Adaptive Network Scaling for Deep Rectifier Reinforcement Learning Models
• Reinforcement Learning with Efficient Active Feature Acquisition
• Policy Smoothing for Provably Robust Reinforcement Learning
• Competitive Multi-Agent Deep Reinforcement Learning with Counterfactual Thinking
• Learning to Navigate Cloth using Haptics
• Phoebe: Reuse-Aware Online Caching with Reinforcement Learning for Emerging Storage Models
• Auto-Agent-Distiller: Towards Efficient Deep Reinforcement Learning Agents via Neural Architecture Search
• Reinforcement Learning Control of Constrained Dynamic Systems with Uniformly Ultimate Boundedness Stability Guarantee
• CRAVES: Controlling Robotic Arm with a Vision-based Economic System
• Inter-Level Cooperation in Hierarchical Reinforcement Learning
• Evolving Neural Networks in Reinforcement Learning by means of UMDAc
• Pretrain Soft Q-Learning with Imperfect Demonstrations
• Policy Search by Target Distribution Learning for Continuous Control
• Towards Theoretical Understandings of Robust Markov Decision Processes: Sample Complexity and Asymptotics
• Target-Based Temporal Difference Learning
• Robust Reinforcement Learning: A Case Study in Linear Quadratic Regulation
• Using Deep Reinforcement Learning to Learn High-Level Policies on the ATRIAS Biped
• Multi-task Learning with Gradient Guided Policy Specialization
• Task-oriented Design through Deep Reinforcement Learning
• Avoidance of Manual Labeling in Robotic Autonomous Navigation Through Multi-Sensory Semi-Supervised Learning
• Teaching UAVs to Race: End-to-End Regression of Agile Controls in Simulation
• Nano: Nested Human-in-the-Loop Reward Learning for Few-shot Language Model Control
• Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed
• Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch
• Bridging the Imitation Gap by Adaptive Insubordination
• Behavior-Guided Actor-Critic: Improving Exploration via Learning Policy Behavior Representation for Deep Reinforcement Learning
• Learning Data Teaching Strategies Via Knowledge Tracing
• Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Control
• Escaping from Zero Gradient: Revisiting Action-Constrained Reinforcement Learning via Frank-Wolfe Policy Optimization
• Attentional Policies for Cross-Context Multi-Agent Reinforcement Learning
• Kernel-based diffusion approximated Markov decision processes for autonomous navigation and control on unstructured terrains
• Distributed Soft Actor-Critic with Multivariate Reward Representation and Knowledge Distillation
• Learning Agents With Prioritization and Parameter Noise in Continuous State and Action Space
• Aggressive Q-Learning with Ensembles: Achieving Both High Sample Efficiency and High Asymptotic Performance
• Exploration via Hindsight Goal Generation
• Reinforcement Mechanism Design for e-commerce
• Danger-aware Adaptive Composition of DRL Agents for Self-navigation
• A Survey on Reproducibility by Evaluating Deep Reinforcement Learning Algorithms on Real-World Robots
• Efficient Navigation of Colloidal Robots in an Unknown Environment via Deep Reinforcement Learning
• An Empirical Analysis of Proximal Policy Optimization with Kronecker-factored Natural Gradients
• Improved Reinforcement Learning through Imitation Learning Pretraining Towards Image-based Autonomous Driving
• A Dual Memory Structure for Efficient Use of Replay Memory in Deep Reinforcement Learning
• Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-Correction
• Deep Reinforcement Learning with Discrete Normalized Advantage Functions for Resource Management in Network Slicing
• TBQ($\sigma$): Improving Efficiency of Trace Utilization for Off-Policy Reinforcement Learning
• Fast Skill Learning for Variable Compliance Robotic Assembly
• A* Tree Search for Portfolio Management
• Towards Physically Safe Reinforcement Learning under Supervision
• Active localization of multiple targets using noisy relative measurements
• Learning Pregrasp Manipulation of Objects from Ungraspable Poses
• End-to-End Vision-Based Adaptive Cruise Control (ACC) Using Deep Reinforcement Learning
• Memristor Hardware-Friendly Reinforcement Learning
• Multi-Robot Formation Control Using Reinforcement Learning
• Functional Error Correction for Robust Neural Networks
• Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning
• Efficient Robotic Task Generalization Using Deep Model Fusion Reinforcement Learning
• Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization
• A Deep Reinforcement Learning Architecture for Multi-stage Optimal Control
• Adaptive Leader-Follower Formation Control and Obstacle Avoidance via Deep Reinforcement Learning
• Coordination of PV Smart Inverters Using Deep Reinforcement Learning for Grid Voltage Regulation
• Learn to Exceed: Stereo Inverse Reinforcement Learning with Concurrent Policy Optimization
• Competitiveness of MAP-Elites against Proximal Policy Optimization on locomotion tasks in deterministic simulations
• Expressing Diverse Human Driving Behavior with Probabilistic Rewards and Online Inference
• DDPG-based Resource Management for MEC/UAV-Assisted Vehicular Networks
• DeepSlicing: Deep Reinforcement Learning Assisted Resource Allocation for Network Slicing
• Multi-robot Cooperative Object Transportation using Decentralized Deep Reinforcement Learning
• Generative Design of Hardware-aware DNNs
• Simulating multi-exit evacuation using deep reinforcement learning
• Generator and Critic: A Deep Reinforcement Learning Approach for Slate Re-ranking in E-commerce
• Towards Automated Safety Coverage and Testing for Autonomous Vehicles with Reinforcement Learning
• Deep Reinforcement Learning Based Spectrum Allocation in Integrated Access and Backhaul Networks
• Sample Efficient Ensemble Learning with Catalyst.RL
• No-Pain No-Gain: DRL Assisted Optimization in Energy-Constrained CR-NOMA Networks
• Self-Imitation Learning by Planning
• Hierarchical deep reinforcement learning controlled three-dimensional navigation of microrobots in blood vessels
• Multi-Agent Reinforcement Learning of 3D Furniture Layout Simulation in Indoor Graphics Scenes
• Deep Reinforcement Learning for Joint Spectrum and Power Allocation in Cellular Networks
• A Multi-intersection Vehicular Cooperative Control based on End-Edge-Cloud Computing
• Fault-Aware Robust Control via Adversarial Reinforcement Learning
• Detecting Adversarial Patches with Class Conditional Reconstruction Networks
• Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning
• Generative Inverse Deep Reinforcement Learning for Online Recommendation
• Smooth Imitation Learning via Smooth Costs and Smooth Policies
• Decentralized Multi-Agent Reinforcement Learning: An Off-Policy Method
• Fully Distributed Actor-Critic Architecture for Multitask Deep Reinforcement Learning
• Feedback Linearization of Car Dynamics for Racing via Reinforcement Learning
• A Simple Approach to Continual Learning by Transferring Skill Parameters
• Improved Soft Actor-Critic: Mixing Prioritized Off-Policy Samples with On-Policy Experience
• A First-Occupancy Representation for Reinforcement Learning
• Flying Through a Narrow Gap Using End-to-end Deep Reinforcement Learning Augmented with Curriculum Learning and Sim2Real
• Scalable Multi-agent Reinforcement Learning Algorithm for Wireless Networks
• An Intelligent Energy Management Framework for Hybrid-Electric Propulsion Systems Using Deep Reinforcement Learning
• On the Robustness of Deep Reinforcement Learning in IRS-Aided Wireless Communications Systems
• Plan-Based Relaxed Reward Shaping for Goal-Directed Tasks
• Embodiment and Computational Creativity
• Boosting Offline Reinforcement Learning with Residual Generative Modeling
• A Deep Reinforcement Learning Approach towards Pendulum Swing-up Problem based on TF-Agents
• Programming and Deployment of Autonomous Swarms using Multi-Agent Reinforcement Learning
• Learning cooperative behaviours in adversarial multi-agent systems
• Energy-Efficient Design for a NOMA assisted STAR-RIS Network with Deep Reinforcement Learning
• Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS
• Improving Learning from Demonstrations by Learning from Experience
• Imaginary Hindsight Experience Replay: Curious Model-based Learning for Sparse Reward Tasks
• Learning Efficient Multi-Agent Cooperative Visual Exploration
• Continuous Control with Action Quantization from Demonstrations
• Provable Regret Bounds for Deep Online Learning and Control
• Double Deep Q-learning Based Real-Time Optimization Strategy for Microgrids
• Gradient Importance Learning for Incomplete Observations
• Aligning an optical interferometer with beam divergence control and continuous action space
• Towards Deeper Deep Reinforcement Learning with Spectral Normalization
• MapGo: Model-Assisted Policy Optimization for Goal-Oriented Tasks
• Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning
• Bias-reduced Multi-step Hindsight Experience Replay for Efficient Multi-goal Reinforcement Learning
• Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space
• Bayesian Meta-reinforcement Learning for Traffic Signal Control
• Learning to Represent Action Values as a Hypergraph on the Action Vertices
• Learning Nash Equilibria in Zero-Sum Stochastic Games via Entropy-Regularized Policy Approximation
• Knowledge-Assisted Deep Reinforcement Learning in 5G Scheduler Design: From Theoretical Framework to Implementation
• Energy-based Surprise Minimization for Multi-Agent Value Factorization
• Follow the Object: Curriculum Learning for Manipulation Tasks with Imagined Goals
• Learning Complex Multi-Agent Policies in Presence of an Adversary
• Learning to Sample with Local and Global Contexts in Experience Replay Buffer
• Learning Compositional Neural Programs for Continuous Control
• Distributed Uplink Beamforming in Cell-Free Networks Using Deep Reinforcement Learning
• Zeroth-Order Supervised Policy Improvement
• Neural Lyapunov Redesign
• Novel Policy Seeking with Constrained Optimization
• Towards Cognitive Routing based on Deep Reinforcement Learning
• Learning a generative model for robot control using visual feedback
• Mid-flight Propeller Failure Detection and Control of Propeller-deficient Quadcopter using Reinforcement Learning
• Information-Theoretic Performance Limitations of Feedback Control: Underlying Entropic Laws and Generic ${\mathcal{L}}_p$ Bounds
• Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online
• Merging Deterministic Policy Gradient Estimations with Varied Bias-Variance Tradeoff for Effective Deep Reinforcement Learning
• Learning Classifiers on Positive and Unlabeled Data with Policy Gradient
• Networked Control of Nonlinear Systems under Partial Observation Using Continuous Deep Q-Learning
• Beyond Exponentially Discounted Sum: Automatic Learning of Return Function
• From semantics to execution: Integrating action planning with reinforcement learning for robotic causal problem-solving
• Baconian: A Unified Open-source Framework for Model-Based Reinforcement Learning
• Personalized Cancer Chemotherapy Schedule: a numerical comparison of performance and robustness in model-based and model-free scheduling methodologies
• Zero-shot Deep Reinforcement Learning Driving Policy Transfer for Autonomous Vehicles based on Robust Control
• Policy Optimization with Model-based Explorations
• ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search
• Using Deep Reinforcement Learning for the Continuous Control of Robotic Arms
• Switching Isotropic and Directional Exploration with Parameter Space Noise in Deep Reinforcement Learning
• Constrained Exploration and Recovery from Experience Shaping
• Learning Adaptive Display Exposure for Real-Time Advertising
• NavigationNet: A Large-scale Interactive Indoor Navigation Dataset
• Intelligent Trainer for Model-Based Reinforcement Learning
• Un résultat intrigant en commande sans modèle
• Shaping in Practice: Training Wheels to Learn Fast Hopping Directly in Hardware
• Semantics, Representations and Grammars for Deep Learning
• Hierarchical RNNs-Based Transformers MADDPG for Mixed Cooperative-Competitive Environments
• MDP Playground: An Analysis and Debug Testbed for Reinforcement Learning
• MACS: Deep Reinforcement Learning based SDN Controller Synchronization Policy Design
• Task-Based Learning via Task-Oriented Prediction Network with Applications in Finance
• InfoRL: Interpretable Reinforcement Learning using Information Maximization
• Graph Pruning for Model Compression
• Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces
• Multi-task Reinforcement Learning with a Planning Quasi-Metric
• TTR-Based Reward for Reinforcement Learning with Implicit Model Priors
• PFPN: Continuous Control of Physically Simulated Characters using Particle Filtering Policy Network
• Deep Reinforcement Learning with Weighted Q-Learning
• Detecting Adversarial Examples in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression
• Flexible and Efficient Long-Range Planning Through Curious Exploration
• Deep Learning: Our Miraculous Year 1990-1991
• Lachesis: Automatic Partitioning for UDF-Centric Analytics
• Parameter-Based Value Functions
• Decentralized Multi-Agents by Imitation of a Centralized Controller
• Extended Radial Basis Function Controller for Reinforcement Learning
• Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles
• A novel control mode of bionic morphing tail based on deep reinforcement learning
• Learning Accurate Extended-Horizon Predictions of High Dimensional Trajectories
• An Active Learning Framework for Efficient Robust Policy Search
• An Accelerated Fitted Value Iteration Algorithm for MDPs with Finite and Vector-Valued Action Space
• Dream and Search to Control: Latent Space Planning for Continuous Control
• Improving the Exploration of Deep Reinforcement Learning in Continuous Domains using Planning for Policy Search
• Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level Paintings
• Towards Neural Knowledge DNA
• Stabilizing Transformer-Based Action Sequence Generation For Q-Learning
• SCAPE: Learning Stiffness Control from Augmented Position Control Experiences
• Safe Learning of Uncertain Environments
• Goal-constrained Sparse Reinforcement Learning for End-to-End Driving
• Don't Forget Your Teacher: A Corrective Reinforcement Learning Framework
• Hybrid Policy Learning for Energy-Latency Tradeoff in MEC-Assisted VR Video Service
• Subgoal-based Reward Shaping to Improve Efficiency in Reinforcement Learning
• Action Candidate Based Clipped Double Q-learning for Discrete and Continuous Action Tasks
• Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients
• Context-Based Soft Actor Critic for Environments with Non-stationary Dynamics
• A Survey on Reinforcement Learning-Aided Caching in Mobile Edge Networks
• Game of GANs: Game-Theoretical Models for Generative Adversarial Networks
• Analysis of a Target-Based Actor-Critic Algorithm with Linear Function Approximation
• On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control
• Learning Altruistic Behaviours in Reinforcement Learning without External Rewards
• Physics-informed Dyna-Style Model-Based Deep Reinforcement Learning for Dynamic Control
• Tolerance-Guided Policy Learning for Adaptable and Transferrable Delicate Industrial Insertion
• Back to Basics: Deep Reinforcement Learning in Traffic Signal Control
• Learning-based Hamilton-Jacobi-Bellman Methods for Optimal Control
• Offline RL With Resource Constrained Online Deployment
• Off-policy Reinforcement Learning with Optimistic Exploration and Distribution Correction
• Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks
• Learning Pessimism for Robust and Efficient Off-Policy Reinforcement Learning
• A Closer Look at Advantage-Filtered Behavioral Cloning in High-Noise Datasets
• Curriculum Offline Imitation Learning
• Analysis of Model-Free Reinforcement Learning Control Schemes on self-balancing Wheeled Extendible System
• Off-Policy Actor-Critic with Emphatic Weightings
• SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition
• Unlocking the Potential of Simulators: Design with RL in Mind
• Continuous Control With Ensemble Deep Deterministic Policy Gradients
• Meta Arcade: A Configurable Environment Suite for Meta-Learning
• Tuning Synaptic Connections instead of Weights by Genetic Algorithm in Spiking Policy Network
• Reliability Quantification of Deep Reinforcement Learning-based Control
• Unsupervised Salient Patch Selection for Data-Efficient Reinforcement Learning
• Worrisome Properties of Neural Network Controllers and Their Symbolic Representations
• A Load Balanced Recommendation Approach
• KnowSR: Knowledge Sharing among Homogeneous Agents in Multi-agent Reinforcement Learning
• Controlling an Inverted Pendulum with Policy Gradient Methods-A Tutorial
• Don't Do What Doesn't Matter: Intrinsic Motivation with Action Usefulness
• Importance Sampling based Exploration in Q Learning
• Low-Dimensional State and Action Representation Learning with MDP Homomorphism Metrics
• HAC Explore: Accelerating Exploration with Hierarchical Reinforcement Learning
• Soft Hierarchical Graph Recurrent Networks for Many-Agent Partially Observable Environments
• ADER:Adapting between Exploration and Robustness for Actor-Critic Methods
• Computation Rate Maximum for Mobile Terminals in UAV-assisted Wireless Powered MEC Networks with Fairness Constraint
• Deictic Image Maps: An Abstraction For Learning Pose Invariant Manipulation Policies
• A Survey of Online Auction Mechanism Design Using Deep Learning Approaches
• SaLinA: Sequential Learning of Agents
• Learning Human Behaviors for Robot-Assisted Dressing
• Policy Optimization with Second-Order Advantage Information
• Coordinated Proximal Policy Optimization
• C-Learning: Horizon-Aware Cumulative Accessibility Estimation
• Deep Reinforcement Learning in Electricity Generation Investment for the Minimization of Long-Term Carbon Emissions and Electricity Costs
• Behavior Planning at Urban Intersections through Hierarchical Reinforcement Learning
• Reinforcement Learning with Time-dependent Goals for Robotic Musicians
• Episodic Self-Imitation Learning with Hindsight
• A survey of benchmarking frameworks for reinforcement learning
• Pareto Deterministic Policy Gradients and Its Application in 5G Massive MIMO Networks
• Towards Recognizing New Semantic Concepts in New Visual Domains
• Optimizing Sponsored Search Ranking Strategy by Deep Reinforcement Learning
• Bootstrapping Motor Skill Learning with Motion Planning
• Instance-Aware Predictive Navigation in Multi-Agent Environments
• Discontinuity-Sensitive Optimal Control Learning by Mixture of Experts
• Comparing Task Simplifications to Learn Closed-Loop Object Picking Using Deep Reinforcement Learning
• Predicting Nanorobot Shapes via Generative Models
• A Hybrid Approach for Reinforcement Learning Using Virtual Policy Gradient for Balancing an Inverted Pendulum
• Measuring Progress in Deep Reinforcement Learning Sample Efficiency
• Learning image quality assessment by reinforcing task amenable data selection
• Online Policy Gradient for Model Free Learning of Linear Quadratic Regulators with $\sqrt{T}$ Regret
• Reducing Conservativeness Oriented Offline Reinforcement Learning
• Decentralized Circle Formation Control for Fish-like Robots in the Real-world via Reinforcement Learning
• Learning Sampling Policy for Faster Derivative Free Optimization
• Data-Driven Reinforcement Learning for Virtual Character Animation Control
• Technical Report: Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning
• How Do You Act? An Empirical Study to Understand Behavior of Deep Reinforcement Learning Agents
• Discover the Hidden Attack Path in Multi-domain Cyberspace Based on Reinforcement Learning
• Discrete-to-Deep Supervised Policy Learning
• Reinforced Coloring for End-to-End Instance Segmentation
• Costate-focused models for reinforcement learning
• Extracting Latent State Representations with Linear Dynamics from Rich Observations
• Weakness Analysis of Cyberspace Configuration Based on Reinforcement Learning
• Tomography Based Learning for Load Distribution through Opaque Networks
• Adaptive Energy Management for Real Driving Conditions via Transfer Reinforcement Learning
• Nintendo Super Smash Bros. Melee: An "Untouchable" Agent
• Expanding Motor Skills through Relay Neural Networks
• Self-Adapting Recurrent Models for Object Pushing from Learning in Simulation
• Compare and Select: Video Summarization with Multi-Agent Reinforcement Learning
• Balanced Order Batching with Task-Oriented Graph Clustering
• How does the structure embedded in learning policy affect learning quadruped locomotion?
• SREC: Proactive Self-Remedy of Energy-Constrained UAV-Based Networks via Deep Reinforcement Learning
• Lyapunov-Based Reinforcement Learning for Decentralized Multi-Agent Control
• EDEN: Enabling Energy-Efficient, High-Performance Deep Neural Network Inference Using Approximate DRAM
• Fast-UAP: An Algorithm for Speeding up Universal Adversarial Perturbation Generation with Orientation of Perturbation Vectors
• Learning to Mix n-Step Returns: Generalizing lambda-Returns for Deep Reinforcement Learning
• Off-Policy Policy Gradient Algorithms by Constraining the State Distribution Shift
• Learning to drive via Apprenticeship Learning and Deep Reinforcement Learning
• IMPACT: Importance Weighted Asynchronous Architectures with Clipped Target Networks
• Multi-Issue Bargaining With Deep Reinforcement Learning
• Discrete Action On-Policy Learning with Action-Value Critic
• Genetic-Gated Networks for Deep Reinforcement
• Deep Hierarchical Reinforcement Learning Based Recommendations via Multi-goals Abstraction
• Learning Policies through Quantile Regression
• NeoNav: Improving the Generalization of Visual Navigation via Generating Next Expected Observations
• Deep Reinforcement Learning Based Robot Arm Manipulation with Efficient Training Data through Simulation
• Prioritized Guidance for Efficient Multi-Agent Reinforcement Learning Exploration
• Deep Reinforcement Learning for Personalized Search Story Recommendation
• A Deep Reinforcement Learning Approach to Multi-component Job Scheduling in Edge Computing
• Reinforcement learning with world model
• Large scale continuous-time mean-variance portfolio allocation via reinforcement learning
• Attention-based Deep Reinforcement Learning for Multi-view Environments
• An online evolving framework for advancing reinforcement-learning based automated vehicle control
• Artificial Buildings: Safety, Complexity and a Quantifiable Measure of Beauty
• Maximum Entropy Model-based Reinforcement Learning
• Deep Reinforcement Learning for Inquiry Dialog Policies with Logical Formula Embeddings
• Sim-to-Real Transfer of Robot Learning with Variable Length Inputs
• Functional Regularization for Reinforcement Learning via Learned Fourier Features
• Environment Shaping in Reinforcement Learning using State Abstraction
• Techniques Toward Optimizing Viewability in RTB Ad Campaigns Using Reinforcement Learning
• Deep Reinforcement Learning for Joint Beamwidth and Power Optimization in mmWave Systems
• Reducing the Deployment-Time Inference Control Costs of Deep Reinforcement Learning Agents via an Asymmetric Architecture
• End-to-End Race Driving with Deep Reinforcement Learning
• Deep Reinforcement Learning with Surrogate Agent-Environment Interface
• Micro/Nano Motor Navigation and Localization via Deep Reinforcement Learning
• Learning to Grasp from 2.5D images: a Deep Reinforcement Learning Approach
• Optimizing Intelligent Reflecting Surface-Base Station Association for Mobile Networks
• Variational Policy Search using Sparse Gaussian Process Priors for Learning Multimodal Optimal Actions
• Deep Reinforcement Learning Models Predict Visual Responses in the Brain: A Preliminary Result
• Discrete linear-complexity reinforcement learning in continuous action spaces for Q-learning algorithms
• FiDi-RL: Incorporating Deep Reinforcement Learning with Finite-Difference Policy Search for Efficient Learning of Continuous Control
• Cautious Actor-Critic
• Dimensionality Reduction of Movement Primitives in Parameter Space
• Influence-Based Reinforcement Learning for Intrinsically-Motivated Agents
• Auto Deep Compression by Reinforcement Learning Based Actor-Critic Structure
• Three-Dimensional Trajectory Design for Multi-User MISO UAV Communications: A Deep Reinforcement Learning Approach
• Parallelized Reverse Curriculum Generation
• Learning Task Agnostic Skills with Data-driven Guidance
• Neural Network Repair with Reachability Analysis
• Mixed Reinforcement Learning with Additive Stochastic Uncertainty
• Responsive Regulation of Dynamic UAV Communication Networks Based on Deep Reinforcement Learning
• Communication-Computation Efficient Device-Edge Co-Inference via AutoML
• Eden: A Unified Environment Framework for Booming Reinforcement Learning Algorithms
• Learning with Training Wheels: Speeding up Training with a Simple Controller for Deep Reinforcement Learning
• Hindsight Reward Tweaking via Conditional Deep Reinforcement Learning
• A Logarithmic Barrier Method For Proximal Policy Optimization
• Learning Vision-Guided Dynamic Locomotion Over Challenging Terrains
• Deep Reinforcement Learning for Equal Risk Pricing and Hedging under Dynamic Expectile Risk Measures
• Optimal Network Control in Partially-Controllable Networks
• DSDF: An approach to handle stochastic agents in collaborative multi-agent reinforcement learning
• DROMO: Distributionally Robust Offline Model-based Policy Optimization
• Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications
• Toward Efficient Federated Learning in Multi-Channeled Mobile Edge Network with Layerd Gradient Compression
• A Model-free Deep Reinforcement Learning Approach To Maneuver A Quadrotor Despite Single Rotor Failure
• Efficiently Training On-Policy Actor-Critic Networks in Robotic Deep Reinforcement Learning with Demonstration-like Sampled Exploration
• Realizing Continual Learning through Modeling a Learning System as a Fiber Bundle
• Learning Neural Parsers with Deterministic Differentiable Imitation Learning
• Multiple-Pilot Collaboration for Advanced Remote Intervention using Reinforcement Learning
• Deep Reinforcement Learning with Adjustments
• Adaptive perturbation adversarial training: based on reinforcement learning
• Biomechanic Posture Stabilisation via Iterative Training of Multi-policy Deep Reinforcement Learning Agents
• Learning Deterministic Policy with Target for Power Control in Wireless Networks
• Locality-Sensitive Experience Replay for Online Recommendation
• Learning Coordinated Tasks using Reinforcement Learning in Humanoids
• An Economy of Neural Networks: Learning from Heterogeneous Experiences
• D2RLIR : an improved and diversified ranking function in interactive recommendation systems based on deep reinforcement learning
• Control of a Nature-inspired Scorpion using Reinforcement Learning
• Models of benthic bipedalism
• Human-Level Control without Server-Grade Hardware
• A Supervised-Learning based Hour-Ahead Demand Response of a Behavior-based HEMS approximating MILP Optimization
• Automatic Goal Generation using Dynamical Distance Learning
• Infer Your Enemies and Know Yourself, Learning in Real-Time Bidding with Partially Observable Opponents
• Continuous Control for High-Dimensional State Spaces: An Interactive Learning Approach
• Deep reinforcement learning for RAN optimization and control
• Soft-Robust Algorithms for Batch Reinforcement Learning
• Dynamic Horizon Value Estimation for Model-based Reinforcement Learning
• A Study of Policy Gradient on a Class of Exactly Solvable Models
• Parameter Critic: a Model Free Variance Reduction Method Through Imperishable Samples
• Cross Learning in Deep Q-Networks
• ACDER: Augmented Curiosity-Driven Experience Replay
• Weighted Entropy Modification for Soft Actor-Critic
• Edge Intelligence for Energy-efficient Computation Offloading and Resource Allocation in 5G Beyond
• Deep Reinforcement Learning and Permissioned Blockchain for Content Caching in Vehicular Edge Computing and Networks
• Towards Learning Controllable Representations of Physical Systems
• Similarity Modeling on Heterogeneous Networks via Automatic Path Discovery
• Self Training Autonomous Driving Agent
• Investigation on the generalization of the Sampled Policy Gradient algorithm
• Multi Pseudo Q-learning Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles
• A Smart Sliding Chinese Pinyin Input Method Editor on Touchscreen
• Reinforcement Learning based Distributed Control of Dissipative Networked Systems
• Suspension Regulation of Medium-low-speed Maglev Trains via Deep Reinforcement Learning
• Conditional Importance Sampling for Off-Policy Learning
• Attention-based Fault-tolerant Approach for Multi-agent Reinforcement Learning Systems
• Convexity and monotonicity in nonlinear optimal control under uncertainty
• Weakly-Supervised Cross-Domain Adaptation for Endoscopic Lesions Segmentation
• Zero-shot Policy Learning with Spatial Temporal RewardDecomposition on Contingency-aware Observation
• State Representation Learning from Demonstration
• MBCAL: Sample Efficient and Variance Reduced Reinforcement Learning for Recommender Systems
• Generative Exploration and Exploitation
• Active Hierarchical Imitation and Reinforcement Learning
• Learning Efficient and Effective Exploration Policies with Counterfactual Meta Policy
• Learning to Prevent Leakage: Privacy-Preserving Inference in the Mobile Cloud
• Learning spatial hearing via innate mechanisms
• Research on Autonomous Maneuvering Decision of UCAV based on Approximate Dynamic Programming
• Long-term Joint Scheduling for Urban Traffic
• When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey
• Continuous Deep Q-Learning with Simulator for Stabilization of Uncertain Discrete-Time Systems
• Accelerating Deep Reinforcement Learning With the Aid of Partial Model: Energy-Efficient Predictive Video Streaming
• CoachNet: An Adversarial Sampling Approach for Reinforcement Learning
• Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising
• Adjust Planning Strategies to Accommodate Reinforcement Learning Agents
• Learning Robust and Adaptive Real-World Continuous Control Using Simulation and Transfer Learning
• RLINK: Deep Reinforcement Learning for User Identity Linkage
• RACE: Reinforced Cooperative Autonomous Vehicle Collision AvoidancE
• A2: Extracting Cyclic Switchings from DOB-nets for Rejecting Excessive Disturbances
• Randomized Policy Learning for Continuous State and Action MDPs
• Generative Adversarial Imagination for Sample Efficient Deep Reinforcement Learning
• Policy Gradient RL Algorithms as Directed Acyclic Graphs
• Unmanned Surface Vehicle Path Planning from the Perspective of Multi-Modality Constraints: A Comprehensive Analysis
• Continuous Multi-objective Zero-touch Network Slicing via Twin Delayed DDPG and OpenAI Gym
• Deep Learning for Wireless Coded Caching with Unknown and Time-Variant Content Popularity
• Congested Urban Networks Tend to Be Insensitive to Signal Settings: Implications for Learning-Based Control
• Deep Reinforcement Learning using Genetic Algorithm for Parameter Optimization
• Lucid Dreaming for Experience Replay: Refreshing Past States with the Current Policy
• Efficient Reinforcement Learning Development with RLzoo
• Planning with Exploration: Addressing Dynamics Bottleneck in Model-based Reinforcement Learning
• Generation of Traffic Flows in Multi-Agent Traffic Simulation with Agent Behavior Model based on Deep Reinforcement Learning
• Playing optical tweezers with deep reinforcement learning: in virtual, physical and augmented environments
• Solving Online Threat Screening Games using Constrained Action Space Reinforcement Learning
• Vision-based Robotic Arm Imitation by Human Gesture
• On the Guaranteed Almost Equivalence between Imitation Learning from Observation and Demonstration
• Deep Reinforcement Learning for Backup Strategies against Adversaries
• Evaluating task-agnostic exploration for fixed-batch learning of arbitrary future tasks
• Reinforcement Learning for Beam Pattern Design in Millimeter Wave and Massive MIMO Systems
• Learning How to Solve Bubble Ball
• Reinforcement learning with distance-based incentive/penalty (DIP) updates for highly constrained industrial control systems
• From Persistent Homology to Reinforcement Learning with Applications for Retail Banking
• Reinforcement Learning for Robust Missile Autopilot Design
• Scheduling and Power Control for Wireless Multicast Systems via Deep Reinforcement Learning
• Multi-Step Recurrent Q-Learning for Robotic Velcro Peeling
• Optimal Control-Based Baseline for Guided Exploration in Policy Gradient Methods
• A selected review on reinforcement learning based control for autonomous underwater vehicles
• HILONet: Hierarchical Imitation Learning from Non-Aligned Observations
• How to Train your Quadrotor: A Framework for Consistently Smooth and Responsive Flight Control via Reinforcement Learning
• Meta Reinforcement Learning with Distribution of Exploration Parameters Learned by Evolution Strategies
• Combining Off and On-Policy Training in Model-Based Reinforcement Learning
• Analyzing the Hidden Activations of Deep Policy Networks: Why Representation Matters
• Improved Cooperation by Exploiting a Common Signal
• Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction
• Reinforcement Learning for Orientation Estimation Using Inertial Sensors with Performance Guarantee
• Maximum Entropy Reinforcement Learning with Mixture Policies
• Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning
• BlockPuzzle - A Challenge in Physical Reasoning and Generalization for Robot Learning
• Model-free Policy Learning with Reward Gradients
• Model Predictive Actor-Critic: Accelerating Robot Skill Acquisition with Deep Reinforcement Learning
• Dynamic Matching Markets in Power Grid: Concepts and Solution using Deep Reinforcement Learning
• A Deep Deterministic Policy Gradient-based Strategy for Stocks Portfolio Management
• Discriminator Augmented Model-Based Reinforcement Learning
• Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational Graph
• GMAC: A Distributional Perspective on Actor-Critic Framework
• Setting Out a Software Stack Capable of Hosting a Virtual ROS-based Competition
• Generative Actor-Critic: An Off-policy Algorithm Using the Push-forward Model
• Combine PPO with NES to Improve Exploration
• Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization
• Solving Heterogeneous General Equilibrium Economic Models with Deep Reinforcement Learning
• Time-Aware Q-Networks: Resolving Temporal Irregularity for Deep Reinforcement Learning
• Learning and Exploring Motor Skills with Spacetime Bounds
• Scalable, Decentralized Multi-Agent Reinforcement Learning Methods Inspired by Stigmergy and Ant Colonies
• Cost-Aware Fine-Grained Recognition for IoTs Based on Sequential Fixations
• TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution
• Iterative Update and Unified Representation for Multi-Agent Reinforcement Learning
• A Dynamics Perspective of Pursuit-Evasion Games of Intelligent Agents with the Ability to Learn
• Doubly Robust Off-Policy Actor-Critic Algorithms for Reinforcement Learning
• Learning to Reach, Swim, Walk and Fly in One Trial: Data-Driven Control with Scarce Data and Side Information
• Automatic Curricula via Expert Demonstrations
• Deep Deterministic Path Following
• Towards Learning to Play Piano with Dexterous Hands and Touch
• Two-stage training algorithm for AI robot soccer
• Going Beyond Linear RL: Sample Efficient Neural Function Approximation
• Crowdfunding Dynamics Tracking: A Reinforcement Learning Approach
• Hierarchical Reinforcement Learning with Optimal Level Synchronization Based on Flow-Based Deep Generative Model
• Reinforcement Learning Architectures: SAC, TAC, and ESAC
• A Policy Efficient Reduction Approach to Convex Constrained Deep Reinforcement Learning
• MBDP: A Model-based Approach to Achieve both Robustness and Sample Efficiency via Double Dropout Planning
• Learning to Advertise for Organic Traffic Maximization in E-Commerce Product Feeds
• Deep Reinforcement Learning for Complex Manipulation Tasks with Sparse Feedback
• Direct Random Search for Fine Tuning of Deep Reinforcement Learning Policies
• cube2net: Efficient Query-Specific Network Construction with Data Cube Organization
• Optimizing Quantum Variational Circuits with Deep Reinforcement Learning
• Solving the Real Robot Challenge using Deep Reinforcement Learning
• POAR: Efficient Policy Optimization via Online Abstract State Representation Learning
• Out-of-the-box channel pruned networks
• Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equations
• Parallel Actors and Learners: A Framework for Generating Scalable RL Implementations
• Is High Variance Unavoidable in RL? A Case Study in Continuous Control
• Training Transition Policies via Distribution Matching for Complex Tasks
• Evolutionary Stochastic Policy Distillation
• ${\mathcal{L}}_1$-$\mathcal{G}\mathcal{P}$: ${\mathcal{L}}_1$ Adaptive Control with Bayesian Learning
• Universal Policies to Learn Them All
• Unbiased Deep Reinforcement Learning: A General Training Framework for Existing and Future Algorithms
• Deep Reinforcement Learning Based Networked Control with Network Delays for Signal Temporal Logic Specifications
• Continuous Multiagent Control using Collective Behavior Entropy for Large-Scale Home Energy Management
• Hierarchy through Composition with Linearly Solvable Markov Decision Processes
• Model-Free $\mu$ Synthesis via Adversarial Reinforcement Learning
• Computing the Feedback Capacity of Finite State Channels using Reinforcement Learning
• DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning
• Hybrid BYOL-ViT: Efficient approach to deal with small datasets
• Efficient Use of heuristics for accelerating XCS-based Policy Learning in Markov Games
• Uncertainty-aware Low-Rank Q-Matrix Estimation for Deep Reinforcement Learning
• Calculus of Consent via MARL: Legitimating the Collaborative Governance Supplying Public Goods
• Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget
• On The Transferability of Deep-Q Networks
• Conditional Neural Architecture Search
• Reinforcement Learning for Volt-Var Control: A Novel Two-stage Progressive Training Strategy
• Explicit Gradient Learning
• SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation
• Potential Field Guided Actor-Critic Reinforcement Learning
• VisualEnv: visual Gym environments with Blender
• Reinforcement Learning in Topology-based Representation for Human Body Movement with Whole Arm Manipulation
• Model Embedding Model-Based Reinforcement Learning
• Adaptive Experience Selection for Policy Gradient