Awesome Deep Reinforcement Learning

Mar 1 2024 update: HILP added

July 2022 update: EDDICT added

Mar 2022 update: a few papers released in early 2022

Dec 2021 update: Unsupervised RL

Introduction to awesome drl

Reinforcement learning is the fundamental framework for building AGI. Therefore we share important contributions within this awesome drl project.

Landscape of Deep RL

updated Landscape of DRL

Content

Illustrations:

Recommendations and suggestions are welcome. ## General guidances * Awesome Offline RL * Reinforcement Learning Today * Multiagent Reinforcement Learning by Marc Lanctot RLSS @ Lille 11 July 2019 * RLDM 2019 Notes by David Abel 11 July 2019 * A Survey of Reinforcement Learning Informed by Natural Language 10 Jun 2019 arxiv * Challenges of Real-World Reinforcement Learning 29 Apr 2019 arxiv * Ray Interference: a Source of Plateaus in Deep Reinforcement Learning 25 Apr 2019 arxiv * Principles of Deep RL by David Silver * University AI’s General introduction to deep rl (in Chinese) * OpenAI’s spinningup * The Promise of Hierarchical Reinforcement Learning 9 Mar 2019 * Deep Reinforcement Learning that Matters 30 Jan 2019 arxiv

2024

2022

Generalist policies

Foundations and theory

General benchmark frameworks

* Android-Env * * MuJoCo | MuJoCo Chinese version * Unsupervised RL Benchmark * Dataset for Offline RL * Spriteworld: a flexible, configurable python-based reinforcement learning environment * Chainerrl Visualizer * Behaviour Suite for Reinforcement Learning 13 Aug 2019 arxiv | code * Quantifying Generalization in Reinforcement Learning 20 Dec 2018 arxiv * S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning 25 Sept 2018 * dopamine * StarCraft II * tfrl * chainerrl * PARL * DI-engine: a generalized decision intelligence engine. It supports various Deep RL algorithms * PPO x Family: Course in Chinese for Deep RL

Unsupervised

Offline

Value based

Policy gradient

Explorations

Actor-Critic

Model-based

Model-free + Model-based

Hierarchical

Option

Connection with other methods

Connecting value and policy methods

Reward design

Unifying

Faster DRL

Multi-agent

New design

Multitask

Observational Learning

Meta Learning

Distributional

Planning

Safety

Inverse RL

No reward RL

Time

Adversarial learning

Use Natural Language

Generative and contrastive representation learning

Belief

PAC

Applications

deeprl.md Github