value function
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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Online POMDP Planning with Anytime Deterministic Guarantees
Autonomous agents operating in real-world scenarios frequently encounter uncertainty and make decisions based on incomplete information.
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Effectively Learning Initiation Sets in Hierarchical Reinforcement Learning
Sutton et al., 2022], is elegantly captured using the options framework [Sutton et al., 1999].
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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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ODE-based Recurrent Model-free Reinforcement Learning for POMDPs
In partially observable (PO) environments, how to infer unseen information from raw observations puzzled the agents.
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