Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action Constraints

• URL: http://arxiv.org/abs/2304.08743v2
• Date: Mon, 29 May 2023 07:39:12 GMT
• Title: Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action Constraints
• Authors: Kazumi Kasaura, Shuwa Miura, Tadashi Kozuno, Ryo Yonetani, Kenta Hoshino, Yohei Hosoe
• Abstract summary: This study presents a benchmark for evaluating action-constrained reinforcement learning (RL) algorithms. We evaluate existing algorithms and their novel variants across multiple robotics control environments.
• Score: 9.293472255463454
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study presents a benchmark for evaluating action-constrained reinforcement learning (RL) algorithms. In action-constrained RL, each action taken by the learning system must comply with certain constraints. These constraints are crucial for ensuring the feasibility and safety of actions in real-world systems. We evaluate existing algorithms and their novel variants across multiple robotics control environments, encompassing multiple action constraint types. Our evaluation provides the first in-depth perspective of the field, revealing surprising insights, including the effectiveness of a straightforward baseline approach. The benchmark problems and associated code utilized in our experiments are made available online at github.com/omron-sinicx/action-constrained-RL-benchmark for further research and development.

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