Safe Deep Model-Based Reinforcement Learning with Lyapunov Functions

• URL: http://arxiv.org/abs/2405.16184v1
• Date: Sat, 25 May 2024 11:21:12 GMT
• Title: Safe Deep Model-Based Reinforcement Learning with Lyapunov Functions
• Authors: Harry Zhang,
• Abstract summary: We propose a new Model-based RL framework to enable efficient policy learning with unknown dynamics. We introduce and explore a novel method for adding safety constraints for model-based RL during training and policy learning.
• Score: 2.50194939587674
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model-based Reinforcement Learning (MBRL) has shown many desirable properties for intelligent control tasks. However, satisfying safety and stability constraints during training and rollout remains an open question. We propose a new Model-based RL framework to enable efficient policy learning with unknown dynamics based on learning model predictive control (LMPC) framework with mathematically provable guarantees of stability. We introduce and explore a novel method for adding safety constraints for model-based RL during training and policy learning. The new stability-augmented framework consists of a neural-network-based learner that learns to construct a Lyapunov function, and a model-based RL agent to consistently complete the tasks while satisfying user-specified constraints given only sub-optimal demonstrations and sparse-cost feedback. We demonstrate the capability of the proposed framework through simulated experiments.

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