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

Kasaura, Kazumi; Miura, Shuwa; Kozuno, Tadashi; Yonetani, Ryo; Hoshino, K.; Hosoe, Yohei

doi:10.1109/lra.2023.3284378

Cited by 6 publications

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Adaptive safe reinforcement learning‐enabled optimization of battery fast‐charging protocols

Chowdhury,

Al‐Wahaibi,

2024

AIChE Journal

View full text Add to dashboard Cite

Optimizing charging protocols is critical for reducing battery charging time and decelerating battery degradation in applications such as electric vehicles. Recently, reinforcement learning (RL) methods have been adopted for such purposes. However, RL‐based methods may not ensure system (safety) constraints, which can cause irreversible damages to batteries and reduce their lifetime. To this end, this article proposes an adaptive and safe RL framework to optimize fast charging strategies while respecting safety constraints with a high probability. In our method, any unsafe action that the RL agent decides will be projected into a safety region by solving a constrained optimization problem. The safety region is constructed using adaptive Gaussian process (GP) models, consisting of static and dynamic GPs, that learn from online experience to adaptively account for any changes in battery dynamics. Simulation results show that our method can charge the batteries rapidly with constraint satisfaction under varying operating conditions.

show abstract