Risk-sensitive Actor-free Policy via Convex Optimization

• URL: http://arxiv.org/abs/2307.00141v1
• Date: Fri, 30 Jun 2023 21:20:04 GMT
• Title: Risk-sensitive Actor-free Policy via Convex Optimization
• Authors: Ruoqi Zhang, Jens Sj\"olund
• Abstract summary: Traditional reinforcement learning methods optimize agents safety, potentially resulting in unintended consequences. We propose an optimal actor-sensitive policy based on a conditional-risk-free network.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional reinforcement learning methods optimize agents without considering safety, potentially resulting in unintended consequences. In this paper, we propose an optimal actor-free policy that optimizes a risk-sensitive criterion based on the conditional value at risk. The risk-sensitive objective function is modeled using an input-convex neural network ensuring convexity with respect to the actions and enabling the identification of globally optimal actions through simple gradient-following methods. Experimental results demonstrate the efficacy of our approach in maintaining effective risk control.

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