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papersTODAY 04:00 UTC

Variance-Penalized Actor-Critic Method Avoids a Second Critic for Risk-Sensitive RL

Researchers propose a nonparametric approach to variance-penalized reinforcement learning that trades expected return for policy stability without training a separate variance critic. The work frames risk-sensitive RL through statistical inference, aiming to cut the extra computation and complexity that online variance estimation usually requires. It is a new arXiv preprint in machine learning.

actor-critic machine-learning reinforcement-learning risk-sensitive-reinforcement-learning statistical-inference variance-penalized-reinforcement-learning

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arXiv cs.LGNonparametric Variance-Penalized Actor-Critic: Statistical Inference for Risk-Sensitive Reinforcement Learning ↗TODAY 04:00 UTC