rl_reach: Reproducible Reinforcement Learning Experiments for Robotic Reaching Tasks

• URL: http://arxiv.org/abs/2102.04916v1
• Date: Tue, 9 Feb 2021 16:14:10 GMT
• Title: rl_reach: Reproducible Reinforcement Learning Experiments for Robotic Reaching Tasks
• Authors: Pierre Aumjaud, David McAuliffe, Francisco Javier Rodr\'iguez Lera, Philip Cardiff
• Abstract summary: We present rl_reach, a self-contained, open-source and easy-to-use software package. It is designed to run reproducible reinforcement learning experiments for customisable robotic reaching tasks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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. rl_reach packs together training environments, agents, hyperparameter optimisation tools and policy evaluation scripts, allowing its users to quickly investigate and identify optimal training configurations. rl_reach is publicly available at this URL: https://github.com/PierreExeter/rl_reach.

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