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Policy gradient for rectangular robust markov decision processes
Policy gradient methods have become a standard for training reinforcement learning agents
in a scalable and efficient manner. However, they do not account for transition uncertainty …
in a scalable and efficient manner. However, they do not account for transition uncertainty …
Soft robust MDPs and risk-sensitive MDPs: Equivalence, policy gradient, and sample complexity
Bridging distributionally robust learning and offline rl: An approach to mitigate distribution shift and partial data coverage
The goal of an offline reinforcement learning (RL) algorithm is to learn optimal polices using
historical (offline) data, without access to the environment for online exploration. One of the …
historical (offline) data, without access to the environment for online exploration. One of the …
Bring your own (non-robust) algorithm to solve robust MDPs by estimating the worst kernel
Robust Markov Decision Processes (RMDPs) provide a framework for sequential decision-
making that is robust to perturbations on the transition kernel. However, current RMDP …
making that is robust to perturbations on the transition kernel. However, current RMDP …
Imprecise probabilities meet partial observability: Game semantics for robust POMDPs
Partially observable Markov decision processes (POMDPs) rely on the key assumption that
probability distributions are precisely known. Robust POMDPs (RPOMDPs) alleviate this …
probability distributions are precisely known. Robust POMDPs (RPOMDPs) alleviate this …
Robust markov decision processes: A place where AI and formal methods meet
Markov decision processes (MDPs) are a standard model for sequential decision-making
problems and are widely used across many scientific areas, including formal methods and …
problems and are widely used across many scientific areas, including formal methods and …