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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 101–110 of 137 papers

TitleStatusHype
Multi-compartment Neuron and Population Encoding Powered Spiking Neural Network for Deep Distributional Reinforcement Learning—0
Near-Minimax-Optimal Distributional Reinforcement Learning with a Generative Model—0
Noise Distribution Decomposition based Multi-Agent Distributional Reinforcement Learning—0
Non-Crossing Quantile Regression for Distributional Reinforcement Learning—0
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning—0
Distributional Model Equivalence for Risk-Sensitive Reinforcement LearningCode0
Distributional Off-policy Evaluation with Bellman Residual MinimizationCode0
A Cramér Distance perspective on Quantile Regression based Distributional Reinforcement LearningCode0
Fully Parameterized Quantile Function for Distributional Reinforcement LearningCode0
Conjugated Discrete Distributions for Distributional Reinforcement LearningCode0
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