PyTorch implementation of classical deep reinforcement learning algorithms

• Vanilla Deep Q-Learning (DQN)

  • Human Level Control Through Deep Reinforement Learning [Publication]

• Double Deep Q-Learning (Double DQN)

  • Deep Reinforcement Learning with Double Q-learning [Publication]

• Dueling Deep Q-Learning (Dueling DQN)

  • Dueling Network Architectures for Deep Reinforcement Learning [Publication]

• Hindsight Experience Replay

  • Hindsight Experience Replay [Publication]

• Prioritized Experience Replay [Publication]

• Synchronous Deep Q-Learning (SDQN)

• REINFORCE

• Deep Deterministic Policy Gradient (DDPG)

  • Continuous control with deep reinforcement learning [Publication]

• Asynchronous/Synchronous Advantage Actor Critic (A3C, A2C) [Publication]

• TD3 [Publication]

• Soft Actor Critic (SAD) [Publication]

• Stacked DQN/DDPG/SAC

• Hindsight Experience Replay [Publication]
• Prioritized Experience Replay [Publication]
• Noisy Networks for Exploration [Publication]

• Efficient Navigation of Active Particles in an Unseen Environment via Deep Reinforcement Learning [Publication]
• 🆕 🔥 Hierarchical planning with deep reinforcement learning for three-dimensional navigation of microrobots in blood vessels (under review)

• 1D stablizer, 2D stabilizer, and multi-Dim stabilizer
• maze with static obstacles and stchastic/deterministic agent
• maze with dynamic obstacles and stchastic/deterministic agent
• finanical portfolio engineering env (for hedging and investment)
• colloidal assembly env
• 🆕 🔥 [3D blood vessel navigation environment]

• [pybullet]

@misc{Yang2019, author = {Yuguang Yang}, title = {DRL-Pytorch}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/yangyutu/DeepReinforcementLearning-PyTorch}} }