On the Sample Complexity of Discounted Reinforcement Learning with Optimized Certainty Equivalents
arXiv:2605.21763v1 Announce Type: new Abstract: We study risk-sensitive reinforcement learning in finite discounted MDPs, where a generative model of the MDP is assumed to be available. We consider a family or risk measures called the optimized certainty equivalent (OCE), which includes important risk measures such as entropic risk, CVaR, and mean-variance. Our focus is on the sample complexities of learning the optimal state-action value function (value learning) and an optimal policy (policy learning) under recursive OCE. We provide an exact characterization of utility functions $u$ for whic
This paper represents continued academic progress in the theoretical underpinnings of advanced reinforcement learning, specifically concerning risk-sensitive decision-making, which is critical for real-world AI applications.
Improved understanding of sample complexity in risk-sensitive reinforcement learning can lead to more robust, efficient, and deployable AI systems, particularly in scenarios requiring reliable and safe operation.
The research advances the theoretical framework for designing and evaluating AI agents that consider various financial or operational risks, potentially making AI more trustworthy in high-stakes environments.
- · AI researchers
- · Autonomous system developers
- · Financial modeling platforms
- · Companies deploying AI in high-risk sectors
- · Teams using simplistic reinforcement learning approaches
Further theoretical advancements in risk-sensitive AI lead to more predictable and robust agent behavior.
These advancements enable the deployment of AI in domains previously considered too risky, such as critical infrastructure or advanced financial trading.
Widespread adoption of risk-aware AI could lead to a systemic increase in the reliability and safety of automated decision-making across various industries, creating new regulatory challenges and opportunities.
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