ScaleRL is a simple and scalable distributed reinforcement learning framework based on Python and PyTorch
- https://github.com/ray-project/ray
- https://github.com/pytorch/rl
- https://github.com/facebookresearch/torchbeast
- https://github.com/facebookresearch/rlmeta
- https://github.com/alex-petrenko/sample-factory
- https://github.com/sjtu-marl/malib.git
- https://github.com/Replicable-MARL/MARLlib
- https://github.com/seolhokim/DistributedRL-Pytorch-Ray.git
- https://www.jiqizhixin.com/articles/2024-02-15-6?from=synced&keyword=%E5%88%86%E5%B8%83%E5%BC%8F%E5%BC%BA%E5%8C%96%E5%AD%A6%E4%B9%A0
- https://joseluisc99.github.io/posts/distributed-reinforcement-learning-a-draft/
[1] Massively Parallel Methods for Deep Reinforcement Learning (SGD, first distributed architecture, Gorilla DQN).
[2] Asynchronous Methods for Deep Reinforcement Learning (SGD, A3C).
[3] Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU (A3C on GPU).
[4] Efficient Parallel Methods for Deep Reinforcement Learning (Batched A2C, GPU).
[5] Evolution Strategies as a Scalable Alternative to Reinforcement Learning (ES).
[6] Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning (ES).
[7] RLlib: Abstractions for Distributed Reinforcement Learning (Library)
[8] Distributed Deep Reinforcement Learning: Learn how to play Atari games in 21 minutes (Batched A3C).
[9] Distributed Prioritized Experience Replay (Ape-X, distributed replay buffer).
[10] IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures (CPU+GPU).
[11] Accelerated Methods for Deep Reinforcement Learning (Simulation Acceleration).
[12] GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning (Simulation Acceleration).
[13] DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames (DD-PPO)
[14] Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning (Sample Factory)
[15] SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference (SEED RL)