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