fabiopardo/tonic

Welcome to the Tonic deep reinforcement learning library. Modularity: Building blocks for creating RL agents, such as models, replays, or exploration strategies, are implemented as configurable modules. Readability: Agents are written in a simple way with an identical API and logs are nicely displayed on the terminal with a progress bar. Fair comparison: The training pipeline is unique and compatible with all Tonic agents and environments. Agents are defined by their core ideas while general tricks/improvements like non-terminal timeouts, observation normalization and action scaling are shared.