Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action Constraints
نویسندگان
چکیده
This study presents a benchmark for evaluating action-constrained reinforcement learning (RL) algorithms. In RL, each action taken by the system must comply with certain constraints. These constraints are crucial ensuring feasibility and safety of actions in real-world systems. We evaluate existing algorithms their novel variants across multiple robotics control environments, encompassing constraint types. Our evaluation provides first in-depth perspective field, revealing surprising insights, including effectiveness straightforward baseline approach. The problems associated code utilized our experiments made available online at github.com/omron-sinicx/action-constrained-RL-benchmark further research development.
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ژورنال
عنوان ژورنال: IEEE robotics and automation letters
سال: 2023
ISSN: ['2377-3766']
DOI: https://doi.org/10.1109/lra.2023.3284378