[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).