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Training reinforcement learning agents at solving a given task is highlydependent on identifying optimal sets of hyperparameters and selecting suitableenvironment input / output configurations. This tedious process could be easedwith a straightforward toolbox allowing its user to quickly compare differenttraining parameter sets. We present rl_reach, a self-contained, open-source andeasy-to-use software package designed to run reproducible reinforcementlearning experiments for customisable robotic reaching tasks. rl_reach packstogether training environments, agents, hyperparameter optimisation tools andpolicy evaluation scripts, allowing its users to quickly investigate andidentify optimal training configurations. rl_reach is publicly available atthis URL: https://github.com/PierreExeter/rl_reach.

rl_reach: Reproducible Reinforcement Learning Experiments for Robotic Reaching Tasks