--- id: "/non-llm-neural-networks-ml-foundations/reinforcement-learning/page/2/" title: "reinforcement learning: 41 curated resources · page 2" url: "https://skillissue.dev/non-llm-neural-networks-ml-foundations/reinforcement-learning/page/2/" --- # reinforcement learning: 41 curated resources reinforcement learning [Beginner](/non-llm-neural-networks-ml-foundations/beginner/index.md) [Intermediate](/non-llm-neural-networks-ml-foundations/intermediate/index.md) [Advanced](/non-llm-neural-networks-ml-foundations/advanced/index.md) [Expert](/non-llm-neural-networks-ml-foundations/expert/index.md) 41 resources · page 2 of 2 - ### [Jumanji](/foundations/llm-fundamentals/jumanji/index.md) by [InstaDeep Ltd](https://instadeep.com) Jumanji documentation: InstaDeep's suite of JAX reinforcement-learning environments for combinatorial and decision problems. Advanced learning_resource docs UK/Tunisia en - ### [MuZero](/foundations/llm-fundamentals/muzero/index.md) by [Julian Schrittwieser et al. (Google DeepMind)](https://deepmind.google/) arXiv paper (2019) introducing MuZero, planning with a learned model to master Atari, Go, chess and shogi. Advanced–Expert learning_resource paper West en - ### [OpenAI Five (Dota 2)](/foundations/llm-fundamentals/openai-five-dota-2/index.md) by [OpenAI](https://openai.com/) arXiv paper (2019) "Dota 2 with Large Scale Deep Reinforcement Learning", describing OpenAI Five's large-scale reinforcement learning for Dota 2. Advanced–Expert learning_resource paper West en - ### [PPO](/foundations/llm-fundamentals/ppo/index.md) by [John Schulman et al. (OpenAI)](https://openai.com/) arXiv paper (2017) "Proximal Policy Optimization Algorithms", introducing PPO, a policy gradient method with a clipped surrogate objective. Advanced–Expert learning_resource paper West en - ### [RL4CO](/foundations/llm-fundamentals/rl4co/index.md) by [AI4CO community (RL4CO)](https://ai4co.org/) RL4CO documentation: reinforcement-learning benchmark and library for combinatorial optimisation problems. Advanced learning_resource docs Global en - ### [SAC](/foundations/llm-fundamentals/sac/index.md) by [Tuomas Haarnoja et al. (UC Berkeley)](https://www.berkeley.edu/) arXiv paper (2018) introducing Soft Actor-Critic (SAC), off-policy maximum-entropy deep reinforcement learning. Advanced–Expert learning_resource paper US en - ### [verifiers v1](/foundations/llm-fundamentals/verifiers-v1/index.md) by [Prime Intellect](https://primeintellect.ai/) Prime Intellect post on verifiers v1, splitting environments into tasksets, harnesses and runtimes for agentic RL and evals. Advanced learning_resource guide US en - ### [EASA AI Concept Paper Proposed Issue 03](/foundations/llm-fundamentals/easa-ai-concept-paper-proposed-issue-03/index.md) by [European Union Aviation Safety Agency (EASA)](https://www.easa.europa.eu/) EASA AI Concept Paper Proposed Issue 03 (PDF), extending aviation AI assurance guidance to more methods and Level 3 automation. Expert learning_resource guide European Union en - ### [Reward Hacking in Reinforcement Learning (Lil'Log)](/foundations/llm-fundamentals/lilian-weng-reward-hacking-in-rl/index.md) by [Lilian](https://lilianweng.github.io) Lilian Weng's survey of reward hacking in reinforcement learning, including in RLHF and coding tasks. Expert learning_resource guide US en [Previous page](/non-llm-neural-networks-ml-foundations/reinforcement-learning/index.md) [Plain-text Markdown copy](/non-llm-neural-networks-ml-foundations/reinforcement-learning/page/2/index.md)