Develops quotient-categorical representations that render the average-reward distributional Bellman operator well-defined, non-expansive, and convergent under i.i.d. and Markovian sampling.
A reinforcement learning method for maximizing undiscounted rewards
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Quotient-Categorical Representations for Bellman-Compatible Average-Reward Distributional Reinforcement Learning
Develops quotient-categorical representations that render the average-reward distributional Bellman operator well-defined, non-expansive, and convergent under i.i.d. and Markovian sampling.