PyTorch implementation of classical deep reinforcement learning algorithms
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Vanilla Deep Q-Learning (DQN)
- Human Level Control Through Deep Reinforement Learning [Publication]
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Double Deep Q-Learning (Double DQN)
- Deep Reinforcement Learning with Double Q-learning [Publication]
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Dueling Deep Q-Learning (Dueling DQN)
- Dueling Network Architectures for Deep Reinforcement Learning [Publication]
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Hindsight Experience Replay
- Hindsight Experience Replay [Publication]
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Prioritized Experience Replay [Publication]
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Synchronous Deep Q-Learning (SDQN)
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REINFORCE
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Deep Deterministic Policy Gradient (DDPG)
- Continuous control with deep reinforcement learning [Publication]
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Asynchronous/Synchronous Advantage Actor Critic (A3C, A2C) [Publication]
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TD3 [Publication]
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Soft Actor Critic (SAD) [Publication]
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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]
- 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
@misc{Yang2019, author = {Yuguang Yang}, title = {DRL-Pytorch}, year = {2019}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/yangyutu/DeepReinforcementLearning-PyTorch}} }