ReAgent is an open source end-to-end platform for applied reinforcement learning (RL) developed and used at Facebook.
ReAgent is built in Python and uses PyTorch for modeling and training and TorchScript for model serving. The platform contains workflows to train popular deep RL algorithms and includes data preprocessing, feature transformation, distributed training, counterfactual policy evaluation, and optimized serving.
For more detailed information about ReAgent, please refer to the whitepaper.
Twin Delayed DDPG (TD3)
Soft Actor-Critic (SAC)
ReAgent can be installed via. pip or manually. Detailed instructions on how to install ReAgent can be found by clicking the link below.
Detailed instructions on how to use ReAgent Models can be found by clicking the link below.
ReAgent Serving Platform (RASP)
The ReAgent Serving Platform (RASP) provides a library that can be embedded into most serving systems and can handle requests at scale. A tutorial is available by clicking the link below.