A Reductions Approach to Risk-Sensitive Reinforcement Learning with Optimized Certainty Equivalents

• URL: http://arxiv.org/abs/2403.06323v2
• Date: Fri, 28 Feb 2025 02:35:06 GMT
• Title: A Reductions Approach to Risk-Sensitive Reinforcement Learning with Optimized Certainty Equivalents
• Authors: Kaiwen Wang, Dawen Liang, Nathan Kallus, Wen Sun,
• Abstract summary: We study risk-sensitive RL where the goal is learn a history-dependent policy that optimize some risk measure of cumulative rewards.<n>We propose two meta-algorithms: one grounded in optimism and another based on policy gradients.<n>We empirically show that our algorithms learn the optimal history-dependent policy in a proof-of-concept MDP.
• Score: 44.09686403685058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study risk-sensitive RL where the goal is learn a history-dependent policy that optimizes some risk measure of cumulative rewards. We consider a family of risks called the optimized certainty equivalents (OCE), which captures important risk measures such as conditional value-at-risk (CVaR), entropic risk and Markowitz's mean-variance. In this setting, we propose two meta-algorithms: one grounded in optimism and another based on policy gradients, both of which can leverage the broad suite of risk-neutral RL algorithms in an augmented Markov Decision Process (MDP). Via a reductions approach, we leverage theory for risk-neutral RL to establish novel OCE bounds in complex, rich-observation MDPs. For the optimism-based algorithm, we prove bounds that generalize prior results in CVaR RL and that provide the first risk-sensitive bounds for exogenous block MDPs. For the gradient-based algorithm, we establish both monotone improvement and global convergence guarantees under a discrete reward assumption. Finally, we empirically show that our algorithms learn the optimal history-dependent policy in a proof-of-concept MDP, where all Markovian policies provably fail.

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