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Deep-BQRL is developed, a model-free distributional reinforcement-learning framework that extends buffered-quantile learning to neural function approximation and attains smaller mean cumulative point-quantile policy gaps than PPO and TRPO at the reported target levels, while UCB-BQRL retains the smallest gaps.
UCB-BQRL is developed, a model-based optimistic learning algorithm that maintains confidence sets for the transition kernel and plans using a lower-buffered quantile criterion, and an information-theoretic lower bound of $\Omega(H/\rho_\tau\sqrt{AT})$ for the regret of any algorithm dealing with a quantile objective fu...
This paper provides the first finite-time convergence guarantees for this algorithm in this setting, for which it is proved that NPG converges sublinearly with a rate of $\mathcal{O}(H^{2}/t)$ after $t$ iterations, where $H$ is the horizon length.
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