Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback

• URL: http://arxiv.org/abs/2307.02842v3
• Date: Mon, 4 Dec 2023 10:37:00 GMT
• Title: Provably Efficient Iterated CVaR Reinforcement Learning with Function Approximation and Human Feedback
• Authors: Yu Chen, Yihan Du, Pihe Hu, Siwei Wang, Desheng Wu, Longbo Huang
• Abstract summary: Risk-sensitive reinforcement learning aims to optimize policies that balance the expected reward and risk. We present a novel framework that employs an Iterated Conditional Value-at-Risk (CVaR) objective under both linear and general function approximations. We propose provably sample-efficient algorithms for this Iterated CVaR RL and provide rigorous theoretical analysis.
• Score: 57.6775169085215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Risk-sensitive reinforcement learning (RL) aims to optimize policies that balance the expected reward and risk. In this paper, we present a novel risk-sensitive RL framework that employs an Iterated Conditional Value-at-Risk (CVaR) objective under both linear and general function approximations, enriched by human feedback. These new formulations provide a principled way to guarantee safety in each decision making step throughout the control process. Moreover, integrating human feedback into risk-sensitive RL framework bridges the gap between algorithmic decision-making and human participation, allowing us to also guarantee safety for human-in-the-loop systems. We propose provably sample-efficient algorithms for this Iterated CVaR RL and provide rigorous theoretical analysis. Furthermore, we establish a matching lower bound to corroborate the optimality of our algorithms in a linear context.

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