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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
For deep RL and the future of AI.
| Date | Stars |
|---|---|
| 2026-07-24 | 1514 |
| 2026-07-25 | 1514 |
| 2026-07-28 | 1514 |
| 2026-07-30 | 1514 |
| 2026-08-06 | 1514 |
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# 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  ## Content - [Awesome Deep Reinforcement Learning](#awesome-deep-reinforcement-learning) - [Introduction to awesome drl](#introduction-to-awesome-drl) - [Landscape of Deep RL](#landscape-of-deep-rl) - [Content](#content) - [General guidances](#general-guidances) - [2022](#2022) - [Foundations and theory](#foundations-and-theory) - [General benchmark frameworks](#general-benchmark-frameworks) - [Unsupervised](#unsupervised) - [Offline](#offline) - [Value based](#value-based) - [Policy gradient](#policy-gradient) - [Explorations](#explorations) - [Actor-Critic](#actor-critic) - [Model-based](#model-based) - [Model-free + Model-based](#model-free--model-based) - [Hierarchical](#hierarchical) - [Option](#option) - [Connection with other methods](#connection-with-other-methods) - [Connecting value and policy methods](#connecting-value-and-policy-methods) - [Reward design](#reward-design) - [Unifying](#unifying) - [Faster DRL](#faster-drl) - [Multi-agent](#multi-agent) - [New design](#new-design) - [Multitask](#multitask) - [Observational Learning](#observational-learning) - [Meta Learning](#meta-learning) - [Distributional](#distributional) - [Planning](#planning) - [Safety](#safety) - [Inverse RL](#inverse-rl) - [No reward RL](#no-reward-rl) - [Time](#time) - [Adversarial learning](#adversarial-learning) - [Use Natural Language](#use-natural-language) - [Generative and contrastive representation learning](#generative-and-contrastive-representation-learning) - [Belief](#belief) - [PAC](#pac) - [Applications](#applications) Illustrations:  **Recommendations and suggestions are welcome**. ## General guidances * [Awesome Offline RL](https://github.com/hanjuku-kaso/awesome-offline-rl) * [Reinforcement Learning Today](http://reinforcementlearning.today/) * [Multiagent Reinforcement Learning by Marc Lanctot RLSS @ Lille](http://mlanctot.info/files/papers/Lanctot_MARL_RLSS2019_Lille.pdf) 11 July 2019 * [RLDM 2019 Notes by David Abel](https://david-abel.github.io/notes/rldm_2019.pdf) 11 July 2019 * [A Survey of Reinforcement Learning Informed by Natural Language](RLNL.md) 10 Jun 2019 [arxiv](https://arxiv.org/pdf/1906.03926.pdf) * [Challenges of Real-World Reinforcement Learning](ChallengesRealWorldRL.md) 29 Apr 2019 [arxiv](https://arxiv.org/pdf/1904.12901.pdf) * [Ray Interference: a Source of Plateaus in Deep Reinforcement Learning](RayInterference.md) 25 Apr 2019 [arxiv](https://arxiv.org/pdf/1904.11455.pdf) * [Principles of Deep RL by David Silver](p10.md) * [University AI's General introduction to deep rl (in Chinese)](https://www.jianshu.com/p/dfd987aa765a) * [OpenAI's spinningup](https://spinningup.openai.com/en/latest/) * [The Promise of Hierarchical Reinforcement Learning](https://thegradient.pub/the-promise-of-hierarchical-reinforcement-learning/) 9 Mar 2019 * [Deep Reinforcement Learning that Matters](reproducing.md) 30 Jan 2019 [arxiv](https://arxiv.org/pdf/1709.06560.pdf) ## 2024 * [Foundation Policies with Hilbert Representations](HILP.md) [arxiv](https://arxiv.org/abs/2402.15567) [repo](https://github.com/seohongpark/HILP) 23 Feb 2024 ## 2022 * Reinforcement Learning with Action-Free Pre-Training from Videos [arxiv](https://arxiv.org/abs/2203.13880) [repo](https://github.com/younggyoseo/apv) ## Generalist policies * [Foundation Policies with Hilbert Representations](HILP.md) [arxiv](https://arxiv.org/abs/2402.15567) [repo](https://github.com/seohongp
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:848eadd921c5dc43, topic:reinforcement-learning, topic:deep-reinforcement-learning, readme:reinforcement learning
