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Found 8 papers, 0 papers with code
Reinforcement Learning in Low-Rank MDPs with Density Features
As a central technical challenge, the additive error of occupancy estimation is incompatible with the multiplicative definition of data coverage.
Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions
Off-policy evaluation often refers to two related tasks: estimating the expected return of a policy and estimating its value function (or other functions of interest, such as density ratios).
Off-Policy Risk Assessment in Markov Decision Processes
To mitigate these problems, we incorporate model-based estimation to develop the first doubly robust (DR) estimator for the CDF of returns in MDPs.
Supervised Learning with General Risk Functionals
Standard uniform convergence results bound the generalization gap of the expected loss over a hypothesis class.
Offline Reinforcement Learning with Realizability and Single-policy Concentrability
Sample-efficiency guarantees for offline reinforcement learning (RL) often rely on strong assumptions on both the function classes (e. g., Bellman-completeness) and the data coverage (e. g., all-policy concentrability).
Off-Policy Risk Assessment in Contextual Bandits
Even when unable to run experiments, practitioners can evaluate prospective policies, using previously logged data.
On the Convergence and Optimality of Policy Gradient for Markov Coherent Risk
Because optimizing the coherent risk is difficult in Markov decision processes, recent work tends to focus on the Markov coherent risk (MCR), a time-consistent surrogate.
