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  • Language
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  • Created about 3 years ago
  • Updated 4 months ago

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Repository Details

Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)
Clean, Robust, and Unified implementation of classical Deep Reinforcement Learning Algorithms


Link of my code:

Python Pytorch DRL


Recommended Resources for DRL

Books:

Online Courses:

Blogs:

Simulation Environments:


Important Papers

DQN: Mnih V, Kavukcuoglu K, Silver D, et al. Human-level control through deep reinforcement learning[J]. nature, 2015, 518(7540): 529-533.

Double DQN: Van Hasselt H, Guez A, Silver D. Deep reinforcement learning with double q-learning[C]//Proceedings of the AAAI conference on artificial intelligence. 2016, 30(1).

PER: Schaul T, Quan J, Antonoglou I, et al. Prioritized experience replay[J]. arXiv preprint arXiv:1511.05952, 2015.

PPO: Schulman J, Wolski F, Dhariwal P, et al. Proximal policy optimization algorithms[J]. arXiv preprint arXiv:1707.06347, 2017.

DDPG: Lillicrap T P, Hunt J J, Pritzel A, et al. Continuous control with deep reinforcement learning[J]. arXiv preprint arXiv:1509.02971, 2015.

TD3: Fujimoto S, Hoof H, Meger D. Addressing function approximation error in actor-critic methods[C]//International conference on machine learning. PMLR, 2018: 1587-1596.

SAC: Haarnoja T, Zhou A, Abbeel P, et al. Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor[C]//International conference on machine learning. PMLR, 2018: 1861-1870.

ASL: Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity


Training Curves of my Code:

Q-learning:

DQN/DDQN on Classic Control:

DQN/DDQN on Atari Game:

Pong Enduro

Prioritized DQN/DDQN on Classic Control:

CartPole LunarLander

PPO Discrete:

PPO Continuous:

DDPG:

Pendulum LunarLanderContinuous

TD3:

SAC Continuous:

SAC Discrete:

Actor-Sharer-Learner (ASL):

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