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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 126–137 of 137 papers

TitleStatusHype
Statistics and Samples in Distributional Reinforcement Learning—0
Distributional reinforcement learning with linear function approximation—0
A Comparative Analysis of Expected and Distributional Reinforcement Learning—0
Information-Directed Exploration for Deep Reinforcement LearningCode0
QUOTA: The Quantile Option Architecture for Reinforcement LearningCode0
Implicit Quantile Networks for Distributional Reinforcement LearningCode0
Nonlinear Distributional Gradient Temporal-Difference Learning—0
GAN Q-learningCode0
Exploration by Distributional Reinforcement Learning—0
An Analysis of Categorical Distributional Reinforcement Learning—0
Distributional Reinforcement Learning with Quantile RegressionCode0
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning—0
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