YsDTL/awesome-deep-rl

This project is for learning and researching on Deep RL. Maintained by University AI researchers.

Awesome Deep Reinforcement Learning project

updated Landscape of DRL

Landscape of DRL

This project is built for people who are learning and researching on latest deep reinforcement learning methods.

Illustrations:

Recommendations and suggestions are welcome.

General guidances

• Principles of Deep RL by David Silver
• general introduction to deep rl (in Chinese)

General Benchmark Testing Frameworks

• S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning 25 Sept 2018
• dopamine
• StarCraft II
• tfrl
• chainerrl

Value based methods

• TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning 8 Mar 2018
• DISTRIBUTED PRIORITIZED EXPERIENCE REPLAY 2 Mar 2018
• Rainbow: Combining Improvements in Deep Reinforcement Learning 6 Oct 2017
• Learning from Demonstrations for Real World Reinforcement Learning 12 Apr 2017
• Dueling Network Architecture
• Double DQN
• Prioritized Experience
• Deep Q-Networks

Policy gradient methods

• Clipped Action Policy Gradient 22 June 2018
• Expected Policy Gradients for Reinforcement Learning 10 Jan 2018
• Proximal Policy Optimization Algorithms 20 July 2017
• Emergence of Locomotion Behaviours in Rich Environments 7 July 2017
• Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning 1 Jun 2017
• Equivalence Between Policy Gradients and Soft Q-Learning
• Trust Region Policy Optimization
• Reinforcement Learning with Deep Energy-Based Policies
• Q-PROP: SAMPLE-EFFICIENT POLICY GRADIENT WITH AN OFF-POLICY CRITIC

Explorations in DRL

• The Uncertainty Bellman Equation and Exploration 15 Sep 2017
• Noisy Networks for Exploration 30 Jun 2017 implementation
• Count-Based Exploration in Feature Space for Reinforcement Learning 25 Jun 2017
• Count-Based Exploration with Neural Density Models 14 Jun 2017
• UCB and InfoGain Exploration via Q-Ensembles 11 Jun 2017
• Minimax Regret Bounds for Reinforcement Learning 16 Mar 2017
• Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models
• EX2: Exploration with Exemplar Models for Deep Reinforcement Learning

Actor-Critic methods

• The Reactor: A Sample-Efficient Actor-Critic Architecture 15 Apr 2017
• SAMPLE EFFICIENT ACTOR-CRITIC WITH EXPERIENCE REPLAY
• REINFORCEMENT LEARNING WITH UNSUPERVISED AUXILIARY TASKS
• Continuous control with deep reinforcement learning

Model-based methods

• Model-Based Stabilisation of Deep Reinforcement Learning 6 Sep 2018
• Learning model-based planning from scratch 19 July 2017

Model-free + Model-based

• Imagination-Augmented Agents for Deep Reinforcement Learning 19 July 2017

Option

• Variational Option Discovery Algorithms 26 July 2018
• A Laplacian Framework for Option Discovery in Reinforcement Learning 16 Jun 2017

Connection with other methods

• Robust Imitation of Diverse Behaviors
• Learning human behaviors from motion capture by adversarial imitation
• Connecting Generative Adversarial Networks and Actor-Critic Methods

Connecting value and policy methods

• Bridging the Gap Between Value and Policy Based Reinforcement Learning
• Policy gradient and Q-learning

Reward design

• Reinforcement Learning with Corrupted Reward Channel 23 May 2017

Unifying

• Multi-step Reinforcement Learning: A Unifying Algorithm

Faster DRL

• Neural Episodic Control

Apply RL to other domains

• TUNING RECURRENT NEURAL NETWORKS WITH REINFORCEMENT LEARNING

Multiagent Settings

• Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning 4 Nov 2018
• INTRINSIC SOCIAL MOTIVATION VIA CAUSAL INFLUENCE IN MULTI-AGENT RL 19 Oct 2018
• Modeling Others using Oneself in Multi-Agent Reinforcement Learning 26 Feb 2018
• The Mechanics of n-Player Differentiable Games 15 Feb 2018
• Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments 10 Oct 2017
• Learning with Opponent-Learning Awareness 13 Sep 2017
• Counterfactual Multi-Agent Policy Gradients
• Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments 7 Jun 2017
• Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games 29 Mar 2017

New design

• IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures 9 Feb 2018
• Reverse Curriculum Generation for Reinforcement Learning
• Trial without Error: Towards Safe Reinforcement Learning via Human Intervention
• Learning to Design Games: Strategic Environments in Deep Reinforcement Learning 5 July 2017

Multitask

• Zero-Shot Task Generalization with Multi-Task Deep Reinforcement Learning 7 Nov 2017
• Distral: Robust Multitask Reinforcement Learning 13 July 2017

Observational Learning

• Observational Learning by Reinforcement Learning 20 Jun 2017

Meta Learning

• Unsupervised Meta-Learning for Reinforcement Learning 12 Jun 2018

Distributional

• GAN Q-learning 20 July 2018
• Implicit Quantile Networks for Distributional Reinforcement Learning 14 Jun 2018
• Nonlinear Distributional Gradient Temporal-Difference Learning 20 May 2018
• DISTRIBUTED DISTRIBUTIONAL DETERMINISTIC POLICY GRADIENTS 23 Apr 2018
• An Analysis of Categorical Distributional Reinforcement Learning 22 Feb 2018
• Distributional Reinforcement Learning with Quantile Regression 27 Oct 2017
• A Distributional Perspective on Reinforcement Learning 21 July 2017

Inverse RL

• ADDRESSING SAMPLE INEFFICIENCY AND REWARD BIAS IN INVERSE REINFORCEMENT LEARNING 9 Sep 2018

Time

• Time Limits in Reinforcement Learning

Applications

• DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character Skills 9 Apr 2018