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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 121–130 of 137 papers

TitleStatusHype
Fully Parameterized Quantile Function for Distributional Reinforcement LearningCode0
Estimating Risk and Uncertainty in Deep Reinforcement LearningCode0
Stochastically Dominant Distributional Reinforcement Learning—0
Distributional Reinforcement Learning for Efficient Exploration—0
GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing—0
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
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