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Found 3 papers, 3 papers with code
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.
Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learning
We formulate this as a distributionally robust reinforcement learning (DR-RL) problem where the objective is to learn the policy which maximizes the value function against the worst possible stochastic model of the environment in an uncertainty set.
Robust Reinforcement Learning using Offline Data
The goal of robust reinforcement learning (RL) is to learn a policy that is robust against the uncertainty in model parameters.
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