An Introduction to Deep Reinforcement Learning

Abstract

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This manuscript provides an introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. We assume the reader is familiar with basic machine learning concepts.

References in corpus (36)

• Scikit-learn: Machine Learning in Python
• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
• A Brief Survey of Deep Reinforcement Learning
• End to End Learning for Self-Driving Cars
• Dueling Network Architectures for Deep Reinforcement Learning
• An Introduction to Deep Reinforcement Learning
• Evolution Strategies as a Scalable Alternative to Reinforcement Learning
• Sequence Level Training with Recurrent Neural Networks
• Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
• Deep reinforcement learning from human preferences
• Count-Based Exploration with Neural Density Models
• Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models
• Towards Biologically Plausible Deep Learning
• Learning by Playing - Solving Sparse Reward Tasks from Scratch
• A Distributional Perspective on Reinforcement Learning
• A Study on Overfitting in Deep Reinforcement Learning
• Hierarchical Solution of Markov Decision Processes using Macro-actions
• Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
• Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning
• Stochastic Neural Networks for Hierarchical Reinforcement Learning
• Equivalence Between Policy Gradients and Soft Q-Learning
• Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning
• Apprenticeship Learning using Inverse Reinforcement Learning and Gradient Methods
• The Predictron: End-To-End Learning and Planning
• Virtual to Real Reinforcement Learning for Autonomous Driving
• Towards Deep Symbolic Reinforcement Learning
• DARLA: Improving Zero-Shot Transfer in Reinforcement Learning
• The Consciousness Prior
• Combining policy gradient and Q-learning
• A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning
• TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games
• Learning model-based planning from scratch
• Neural Programmer: Inducing Latent Programs with Gradient Descent
• Recurrent Reinforcement Learning: A Hybrid Approach
• Value Prediction Network
• Beating Atari with Natural Language Guided Reinforcement Learning

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• Interactive Groupwise Comparison for Reinforcement Learning from Human Feedback
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