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Distributional Reinforcement Learning

Value distribution is the distribution of the random return received by a reinforcement learning agent. it been used for a specific purpose such as implementing risk-aware behaviour.

We have random return Z whose expectation is the value Q. This random return is also described by a recursive equation, but one of a distributional nature

Papers

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Showing 81–90 of 137 papers

TitleStatusHype
Distributional Reinforcement Learning with Regularized Wasserstein LossCode0
On solutions of the distributional Bellman equation—0
Conservative Distributional Reinforcement Learning with Safety Constraints—0
Robustness and risk management via distributional dynamic programming—0
Conjugated Discrete Distributions for Distributional Reinforcement LearningCode0
Two steps to risk sensitivityCode0
Distributional Reinforcement Learning for Multi-Dimensional Reward FunctionsCode0
The Benefits of Being Categorical Distributional: Uncertainty-aware Regularized Exploration in Reinforcement Learning—0
A Cramér Distance perspective on Quantile Regression based Distributional Reinforcement LearningCode0
Distributional Perturbation for Efficient Exploration in Distributional Reinforcement Learning—0
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