Welcome to People’s Reinforcement Learning (PRL) documentation!

Our main goal is to build a useful tool for the reinforcement learning researchers.

While using PRL library for building agents and conducting experiments you can focus on a structure of an agent, state transformations, neural networks architecture, action transformations and reward shaping. Time and memory profiling, logging, agent-environment interactions, agent state saving, neural network training, early stopping or training visualization happens automatically behind the scenes. You are also provided with very useful tools for handling training history and preparing training sets for neural networks.