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Framework for deep reinforcement learning.
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DeepRL
samples
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LICENSE
README.md
setup.py

README.md

DeepRL

Framework for deep reinforcement learning.

Features:

  • Algorithms are splited into modules
  • Easy to run algorithms asynchronously
  • Easy to add new algorithms

Dependences

  • python3.6
  • numpy
  • pytorch
  • gym

Install

  1. git clone https://github.com/ppaanngggg/DeepRL
  2. pip install -e .

Modules:

1. Agent

  • DoubleDQNAgent: Basic deep Q learning with double Q learning

    Human-level control through deep reinforcement learning

    Deep Reinforcement Learning with Double Q-learning

  • DDPGAgent: continue control by deep deterministic policy gradient

    CONTINUOUS CONTROL WITH DEEP REINFORCEMENT LEARNING

  • PPOAgent: continue control by proximal policy optimization

    Proximal Policy Optimization Algorithms

2. Replay

  • Replay: Basic replay, randomly choose from pool and remove the oldest one

    Human-level control through deep reinforcement learning

  • ReservoirReplay: randomly choose from pool and randomly remove one, used in NFSPAgent's policy network

    Deep Reinforcement Learning from Self-Play in Imperfect-Information Games

  • TmpReplay: just for module, no replay at all

3. Train

  • Train: normal trainer
  • TrainEpoch:
  • AsynTrainEpoch: it will

4. Env

  • EnvAbstract: Env interface, similar to gym's interfaces. User has to reimplement interface functions

TODO

  1. turn python2 to python3.6
  2. turn tensorflow to pytorch
  3. add more agent
  4. well doc
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