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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 61–70 of 137 papers

TitleStatusHype
Non-Crossing Quantile Regression for Distributional Reinforcement Learning—0
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning—0
Nonlinear Distributional Gradient Temporal-Difference Learning—0
Normality-Guided Distributional Reinforcement Learning for Continuous Control—0
Offline and Distributional Reinforcement Learning for Radio Resource Management—0
Offline and Distributional Reinforcement Learning for Wireless Communications—0
One-Step Distributional Reinforcement Learning—0
On Policy Evaluation Algorithms in Distributional Reinforcement Learning—0
On solutions of the distributional Bellman equation—0
PACER: A Fully Push-forward-based Distributional Reinforcement Learning Algorithm—0
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