Deep reinforcement learning under signal temporal logic constraints using Lagrangian relaxation

• URL: http://arxiv.org/abs/2201.08504v1
• Date: Fri, 21 Jan 2022 00:56:25 GMT
• Title: Deep reinforcement learning under signal temporal logic constraints using Lagrangian relaxation
• Authors: Junya Ikemoto and Toshimitsu Ushio
• Abstract summary: In general, a constraint may be imposed on the decision making. We consider the optimal decision making problems with constraints to complete temporal high-level tasks. We propose a two-phase constrained DRL algorithm using the Lagrangian relaxation method.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep reinforcement learning (DRL) has attracted much attention as an approach to solve sequential decision making problems without mathematical models of systems or environments. In general, a constraint may be imposed on the decision making. In this study, we consider the optimal decision making problems with constraints to complete temporal high-level tasks in the continuous state-action domain. We describe the constraints using signal temporal logic (STL), which is useful for time sensitive control tasks since it can specify continuous signals within a bounded time interval. To deal with the STL constraints, we introduce an extended constrained Markov decision process (CMDP), which is called a $\tau$-CMDP. We formulate the STL constrained optimal decision making problem as the $\tau$-CMDP and propose a two-phase constrained DRL algorithm using the Lagrangian relaxation method. Through simulations, we also demonstrate the learning performance of the proposed algorithm.

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