Model-Free Reinforcement Learning for Optimal Control of MarkovDecision Processes Under Signal Temporal Logic Specifications

• URL: http://arxiv.org/abs/2109.13377v1
• Date: Mon, 27 Sep 2021 22:44:55 GMT
• Title: Model-Free Reinforcement Learning for Optimal Control of MarkovDecision Processes Under Signal Temporal Logic Specifications
• Authors: Krishna C. Kalagarla, Rahul Jain, Pierluigi Nuzzo
• Abstract summary: We present a model-free reinforcement learning algorithm to find an optimal policy for a finite-horizon Markov decision process. We illustrate the effectiveness of our approach in the context of robotic motion planning for complex missions under uncertainty and performance objectives.
• Score: 7.842869080999489
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a model-free reinforcement learning algorithm to find an optimal policy for a finite-horizon Markov decision process while guaranteeing a desired lower bound on the probability of satisfying a signal temporal logic (STL) specification. We propose a method to effectively augment the MDP state space to capture the required state history and express the STL objective as a reachability objective. The planning problem can then be formulated as a finite-horizon constrained Markov decision process (CMDP). For a general finite horizon CMDP problem with unknown transition probability, we develop a reinforcement learning scheme that can leverage any model-free RL algorithm to provide an approximately optimal policy out of the general space of non-stationary randomized policies. We illustrate the effectiveness of our approach in the context of robotic motion planning for complex missions under uncertainty and performance objectives.

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