Action Mapping for Reinforcement Learning in Continuous Environments with Constraints

• URL: http://arxiv.org/abs/2412.04327v1
• Date: Thu, 05 Dec 2024 16:42:45 GMT
• Title: Action Mapping for Reinforcement Learning in Continuous Environments with Constraints
• Authors: Mirco Theile, Lukas Dirnberger, Raphael Trumpp, Marco Caccamo, Alberto L. Sangiovanni-Vincentelli,
• Abstract summary: We propose a novel DRL training strategy utilizing action mapping to streamline the learning process.<n>We demonstrate through experiments that action mapping significantly improves training performance in constrained environments.
• Score: 4.521631014571241
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep reinforcement learning (DRL) has had success across various domains, but applying it to environments with constraints remains challenging due to poor sample efficiency and slow convergence. Recent literature explored incorporating model knowledge to mitigate these problems, particularly through the use of models that assess the feasibility of proposed actions. However, integrating feasibility models efficiently into DRL pipelines in environments with continuous action spaces is non-trivial. We propose a novel DRL training strategy utilizing action mapping that leverages feasibility models to streamline the learning process. By decoupling the learning of feasible actions from policy optimization, action mapping allows DRL agents to focus on selecting the optimal action from a reduced feasible action set. We demonstrate through experiments that action mapping significantly improves training performance in constrained environments with continuous action spaces, especially with imperfect feasibility models.

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