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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
Exploration by Distributional Reinforcement Learning—0
Exploration with Multi-Sample Target Values for Distributional Reinforcement Learning—0
Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations—0
A Finite Sample Analysis of Distributional TD Learning with Linear Function Approximation—0
Flow Models for Unbounded and Geometry-Aware Distributional Reinforcement Learning—0
Foundations of Multivariate Distributional Reinforcement Learning—0
GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing—0
A Simulation Environment and Reinforcement Learning Method for Waste Reduction—0
Hedging and Pricing Structured Products Featuring Multiple Underlying Assets—0
How Does Return Distribution in Distributional Reinforcement Learning Help Optimization?—0
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