rl_reach: Reproducible Reinforcement Learning Experiments for Robotic Reaching Tasks

Training reinforcement learning agents at solving a given task is highly dependent on identifying optimal sets of hyperparameters and selecting suitable environment input / output configurations. This tedious process could be eased with a straightforward toolbox allowing its user to quickly compare different training parameter sets. We present rl_reach, a self-contained, open-source and easy-to-use software package designed to run reproducible reinforcement learning experiments for customisable robotic reaching tasks. MIT License Code versioning system used git Software code language used Python 3 Compilation requirements & dependencies Docker OR Python 3, Conda, CUDA (optional) Link to developer documentation/manual https://rl-reach.readthedocs.io/en/latest/index.html Support email for questions pierre.aumjaud@ucd.ie Industrial processes have seen their productivity and efficiency increase considerably in recent decades thanks to the automation of repetitive tasks, notably with the advances in robotics. This productivity can be further improved by enabling robotic agents to solve tasks independently, without being explicitly programmed by humans.