AI RESEARCH
Continuous-time Risk-sensitive Reinforcement Learning via Quadratic Variation Penalty
arXiv CS.LG
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ArXi:2404.12598v2 Announce Type: replace This paper studies continuous-time risk-sensitive reinforcement learning (RL) under the entropy-regularized, exploratory diffusion process formulation with the exponential-form objective. The risk-sensitive objective arises either as the agent's risk attitude or as a distributionally robust approach against the model uncertainty.