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.
Distributional reinforce- ment learning with quantile regression
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.