dm_control: Software and Tasks for Continuous Control
arXiv:2006.12983 · doi:10.1016/j.simpa.2020.100022
Abstract
The dm_control software package is a collection of Python libraries and task suites for reinforcement learning agents in an articulated-body simulation. A MuJoCo wrapper provides convenient bindings to functions and data structures. The PyMJCF and Composer libraries enable procedural model manipulation and task authoring. The Control Suite is a fixed set of tasks with standardised structure, intended to serve as performance benchmarks. The Locomotion framework provides high-level abstractions and examples of locomotion tasks. A set of configurable manipulation tasks with a robot arm and snap-together bricks is also included. dm_control is publicly available at https://www.github.com/deepmind/dm_control
arXiv admin note: text overlap with arXiv:1801.00690
References in corpus (17)
- Continuous control with deep reinforcement learning
- Trust Region Policy Optimization
- Asynchronous Methods for Deep Reinforcement Learning
- Benchmarking Deep Reinforcement Learning for Continuous Control
- Emergence of Locomotion Behaviours in Rich Environments
- DeepMind Control Suite
- Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
- Maximum a Posteriori Policy Optimisation
- Learning human behaviors from motion capture by adversarial imitation
- Reinforcement and Imitation Learning for Diverse Visuomotor Skills
- Neural probabilistic motor primitives for humanoid control
- Emergent Coordination Through Competition
- Hierarchical visuomotor control of humanoids
- DeepMind Lab
- Learning Awareness Models
- IKEA Furniture Assembly Environment for Long-Horizon Complex Manipulation Tasks
- Simultaneously Learning Vision and Feature-based Control Policies for Real-world Ball-in-a-Cup
Cited by in corpus (40)
- Embodied Intelligence via Learning and Evolution
- A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems
- Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning
- ManiSkill: Generalizable Manipulation Skill Benchmark with Large-Scale Demonstrations
- Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning
- Knowledge Transfer for Cross-Domain Reinforcement Learning: A Systematic Review
- Sample-efficient Reinforcement Learning Representation Learning with Curiosity Contrastive Forward Dynamics Model
- State Entropy Maximization with Random Encoders for Efficient Exploration
- StARformer: Transformer with State-Action-Reward Representations for Visual Reinforcement Learning
- MBRL-Lib: A Modular Library for Model-based Reinforcement Learning
- PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training
- CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement Learning
- Behavior From the Void: Unsupervised Active Pre-Training
- MADE: Exploration via Maximizing Deviation from Explored Regions
- Learning and Planning in Complex Action Spaces
- SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning
- Deep Reinforcement Learning for Concentric Tube Robot Path Following
- rl_reach: Reproducible Reinforcement Learning Experiments for Robotic Reaching Tasks
- Learning Markov State Abstractions for Deep Reinforcement Learning
- Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse
- Physically Embedded Planning Problems: New Challenges for Reinforcement Learning
- Reward Bonuses with Gain Scheduling Inspired by Iterative Deepening Search
- Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments
- A Comparative Tutorial of Bayesian Sequential Design and Reinforcement Learning
- Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies
- SILG: The Multi-environment Symbolic Interactive Language Grounding Benchmark
- Building Minimal and Reusable Causal State Abstractions for Reinforcement Learning
- : Analysis of High-Dimensional Robot Controllers via Topological Tools in a Latent Space
- Eigensubspace of Temporal-Difference Dynamics and How It Improves Value Approximation in Reinforcement Learning
- Learning Temporally-Consistent Representations for Data-Efficient Reinforcement Learning
- Multi-Step First: A Lightweight Deep Reinforcement Learning Strategy for Robust Continuous Control with Partial Observability
- Revisiting Experience Replayable Conditions
- Intentionally-underestimated Value Function at Terminal State for Temporal-difference Learning with Mis-designed Reward
- Hierarchical Skills for Efficient Exploration
- Evaluating model-based planning and planner amortization for continuous control
- Phase-Amplitude Reduction-Based Imitation Learning
- Active Offline Policy Selection
- KeyCLD: Learning Constrained Lagrangian Dynamics in Keypoint Coordinates from Images
- Dynamic Horizon Value Estimation for Model-based Reinforcement Learning
- Multitask Adaptation by Retrospective Exploration with Learned World Models