A framework for training and evaluating Reinforcement Learning (RL) algorithms. Built with Python, TensorFlow framework and OpenAI Gym interface.
🚧 Currently under heavy development and some of its components may suffer from instability issues.
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git clone https://github.com/dbobrenko/reinforceflow.git -
cd reinforceflow -
pip install -e .[tf-gpu]In case if you have no CUDA device available, use CPU-only TensorFlow:
pip install -e .[tf] -
To get examples working, install Gym by following the instructions at OpenAI Gym repo;
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(Optional) For more environments, you can install OpenAI Universe;
Examples can be found in examples directory:
# To train A3C on Breakout, see:
python examples/a3c/breakout.py
# To train Async DeepQ on Pong, see:
python examples/asyncdeepq/pong.py
# To train DQN on CartPole:
python examples/dqn/cartpole.py
# etc.
- DQN: Human-level control through deep reinforcement learning
- Double DQN: Deep Reinforcement Learning with Double Q-learning
- Dueling DQN Dueling Network Architectures for Deep Reinforcement Learning
- Prioritized Experience Replay
- Async DQN: Asynchronous Methods for Deep Reinforcement Learning
- A3C: Asynchronous Methods for Deep Reinforcement Learning