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.

1. git clone https://github.com/dbobrenko/reinforceflow.git

2. cd reinforceflow

3. pip install -e .[tf-gpu]

   In case if you have no CUDA device available, use CPU-only TensorFlow:

   pip install -e .[tf]

4. To get examples working, install Gym by following the instructions at OpenAI Gym repo;

5. (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

• OpenAI Baselines
• TensorForce
• Keras-rl
• OpenAI rllab