optimal policy
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Appendix 495 T able of Contents 496 497 A Other related works 14 498 B Preliminaries 15 499
First, note that we shall use the same MDPs defined in Appendix D.1 as follows
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Iterative Reachability Estimation for Safe Reinforcement Learning
We theoretically establish that our algorithms almost surely converge to locally optimal policies of our safe optimization framework.
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Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation
Please refer to the website for more details.
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Survival Instinct in Offline Reinforcement Learning
This phenomenon cannot be easily explained by offline RL's return maximization objective.
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Distributional Model Equivalence for Risk-Sensitive Reinforcement Learning Tyler Kastner
We consider the problem of learning models for risk-sensitive reinforcement learning.
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