Data-Driven Evaluation of Training Action Space for Reinforcement Learning

• URL: http://arxiv.org/abs/2204.03840v1
• Date: Fri, 8 Apr 2022 04:53:43 GMT
• Title: Data-Driven Evaluation of Training Action Space for Reinforcement Learning
• Authors: Rajat Ghosh, Debojyoti Dutta
• Abstract summary: This paper proposes a Shapley-inspired methodology for training action space categorization and ranking. To reduce exponential-time shapley computations, the methodology includes a Monte Carlo simulation. The proposed data-driven methodology is RL to different domains, use cases, and reinforcement learning algorithms.
• Score: 1.370633147306388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training action space selection for reinforcement learning (RL) is conflict-prone due to complex state-action relationships. To address this challenge, this paper proposes a Shapley-inspired methodology for training action space categorization and ranking. To reduce exponential-time shapley computations, the methodology includes a Monte Carlo simulation to avoid unnecessary explorations. The effectiveness of the methodology is illustrated using a cloud infrastructure resource tuning case study. It reduces the search space by 80\% and categorizes the training action sets into dispensable and indispensable groups. Additionally, it ranks different training actions to facilitate high-performance yet cost-efficient RL model design. The proposed data-driven methodology is extensible to different domains, use cases, and reinforcement learning algorithms.

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