资源算法DeepRL_PyTorch

DeepRL_PyTorch

2020-02-13 | |  57 |   0 |   0

Deep Reinforcement Learning Codes

Currently there are only the codes for distributional reinforcement learning here. Codes for algorithms: DQN, C51, QR-DQN, IQN, QUOTA.

Thanks to sungyubkim and Shangtong Zhang!

A lot of my codes references their implementations.

Always up for a chat -- shoot me an email if you'd like to discuss anything!

Dependency:

  • pytorch(>=1.0.0)

  • gym(=0.10.9)

  • numpy

  • matplotlib

Usage:

When your computer's python environment satisfies the above dependencies, you can run the code. For example, enter:

python 4_iqn.py Breakout

on the command line to run the algorithms in the Atari environment. You can change some specific parameters for the algorithms inside the codes.

References:

  1. Human-level control through deep reinforcement learning(DQN) [Paper] [Code]

  2. A Distributional Perspective on Reinforcement Learning(C51) [Paper] [Code]

  3. Distributional Reinforcement Learning with Quantile Regression(QR-DQN) [Paper] [Code]

  4. Implicit Quantile Networks for Distributional Reinforcement Learning(IQN) [Paper] [Code]

  5. QUOTA: The Quantile Option Architecture for Reinforcement Learning(QUOTA) [Paper] [Code]


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