60,345 research outputs found
Epistemic risk-sensitive reinforcement learning
We develop a framework for risk-sensitive behaviour in reinforcement learning (RL) due to uncertainty about the environment dynamics by leveraging utility-based definitions of risk sensitivity. In this framework, the preference for risk can be tuned by varying the utility function, for which we develop dynamic programming (DP) and policy gradient-based algorithms. The risk-averse behavior is compared with the behavior of risk-neutral policy in environments with epistemic risk
Regret Bounds for Risk-sensitive Reinforcement Learning with Lipschitz Dynamic Risk Measures
We study finite episodic Markov decision processes incorporating dynamic risk
measures to capture risk sensitivity. To this end, we present two model-based
algorithms applied to \emph{Lipschitz} dynamic risk measures, a wide range of
risk measures that subsumes spectral risk measure, optimized certainty
equivalent, distortion risk measures among others. We establish both regret
upper bounds and lower bounds. Notably, our upper bounds demonstrate optimal
dependencies on the number of actions and episodes, while reflecting the
inherent trade-off between risk sensitivity and sample complexity.
Additionally, we substantiate our theoretical results through numerical
experiments
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