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Deep reinforcement learning has been one of the fastest growing fields ofmachine learning over the past years and numerous libraries have been opensourced to support research. However, most codebases have a steep learningcurve or limited flexibility that do not satisfy a need for fast prototyping infundamental research. This paper introduces Tonic, a Python library allowingresearchers to quickly implement new ideas and measure their importance byproviding: 1) general-purpose configurable modules 2) several baseline agents:A2C, TRPO, PPO, MPO, DDPG, D4PG, TD3 and SAC built with these modules 3)support for TensorFlow 2 and PyTorch 4) support for continuous-controlenvironments from OpenAI Gym, DeepMind Control Suite and PyBullet 5) scripts toexperiment in a reproducible way, plot results, and play with trained agents 6)a benchmark of the provided agents on 70 continuous-control tasks. Evaluationis performed in fair conditions with identical seeds, training and testingloops, while sharing general improvements such as non-terminal timeouts andobservation normalization. Finally, to demonstrate how Tonic simplifiesexperimentation, a novel agent called TD4 is implemented and evaluated.

Tonic: A Deep Reinforcement Learning Library for Fast Prototyping and Benchmarking