Model-based Chance-Constrained Reinforcement Learning via Separated Proportional-Integral Lagrangian

• URL: http://arxiv.org/abs/2108.11623v1
• Date: Thu, 26 Aug 2021 07:34:14 GMT
• Title: Model-based Chance-Constrained Reinforcement Learning via Separated Proportional-Integral Lagrangian
• Authors: Baiyu Peng, Jingliang Duan, Jianyu Chen, Shengbo Eben Li, Genjin Xie, Congsheng Zhang, Yang Guan, Yao Mu, Enxin Sun
• Abstract summary: We propose a separated proportional-integral Lagrangian algorithm to enhance RL safety under uncertainty. We demonstrate our method can reduce the oscillations and conservatism of RL policy in a car-following simulation.
• Score: 5.686699342802045
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Safety is essential for reinforcement learning (RL) applied in the real world. Adding chance constraints (or probabilistic constraints) is a suitable way to enhance RL safety under uncertainty. Existing chance-constrained RL methods like the penalty methods and the Lagrangian methods either exhibit periodic oscillations or learn an over-conservative or unsafe policy. In this paper, we address these shortcomings by proposing a separated proportional-integral Lagrangian (SPIL) algorithm. We first review the constrained policy optimization process from a feedback control perspective, which regards the penalty weight as the control input and the safe probability as the control output. Based on this, the penalty method is formulated as a proportional controller, and the Lagrangian method is formulated as an integral controller. We then unify them and present a proportional-integral Lagrangian method to get both their merits, with an integral separation technique to limit the integral value in a reasonable range. To accelerate training, the gradient of safe probability is computed in a model-based manner. We demonstrate our method can reduce the oscillations and conservatism of RL policy in a car-following simulation. To prove its practicality, we also apply our method to a real-world mobile robot navigation task, where our robot successfully avoids a moving obstacle with highly uncertain or even aggressive behaviors.

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