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Apr 1, 2024 · The paper provides an overview of the theory and applications of risk-sensitive Markov decision processes.
Nov 12, 2023 · Abstract. The paper provides an overview of the theory and applications of risk-sensitive. Markov decision processes. The term 'risk-sensitive' refers here ...
May 1, 2024 · We suggest the Risk-averse Reinforcement Learning model, a decision-making technique in deep learning with risk constraints for portfolio optimization.
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Jun 4, 2024 · As risk escalates, expected returns also rise, albeit at a diminishing rate. This underscores the diminishing returns of assuming additional risk. Moreover ...
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Jan 25, 2024 · Abstract. We study the regret guarantee for risk-sensitive reinforcement learning (RSRL) via distri- butional reinforcement learning (DRL) methods.
Sep 11, 2024 · The primary goal of reinforcement learning is to develop decision-making policies that prioritize optimal performance without considering risk or safety. In ...
Nov 5, 2023 · Instead of optimizing the expectation of return, risk-sensitive RL optimizes a risk measure based on a return distribution. Risk-sensitive value factorization ...
Apr 23, 2024 · Using risk-sensitive reinforcement learning, portfolio managers can optimize their invest- ment strategies by balancing risk and return to meet their clients' ...
Apr 18, 2024 · This paper addresses the challenges in planning trajectories for robotic arm manipulators in dynamic environments.
Oct 11, 2023 · Decision theorists have offered competing accounts of decision-making procedures that incorporate risk sensitivity. We explore three different kinds of ...