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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

Showing 2130 of 137 papers

TitleStatusHype
Distributional Reinforcement Learning for Multi-Dimensional Reward FunctionsCode0
Distributional Reinforcement Learning with Quantile RegressionCode0
Echoes of Socratic Doubt: Embracing Uncertainty in Calibrated Evidential Reinforcement LearningCode0
Conjugated Discrete Distributions for Distributional Reinforcement LearningCode0
Distributional Model Equivalence for Risk-Sensitive Reinforcement LearningCode0
A Cramér Distance perspective on Quantile Regression based Distributional Reinforcement LearningCode0
ADDQ: Adaptive Distributional Double Q-LearningCode0
CTD4 -- A Deep Continuous Distributional Actor-Critic Agent with a Kalman Fusion of Multiple CriticsCode0
Distributional Off-policy Evaluation with Bellman Residual MinimizationCode0
Distributional Bellman Operators over Mean EmbeddingsCode0
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