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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 71–80 of 137 papers

TitleStatusHype
Distributional constrained reinforcement learning for supply chain optimizationCode0
Multi-compartment Neuron and Population Encoding Powered Spiking Neural Network for Deep Distributional Reinforcement Learning—0
An Analysis of Quantile Temporal-Difference Learning—0
Invariance to Quantile Selection in Distributional Continuous Control—0
Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds—0
How Does Return Distribution in Distributional Reinforcement Learning Help Optimization?—0
Normality-Guided Distributional Reinforcement Learning for Continuous Control—0
The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning—0
Risk Perspective Exploration in Distributional Reinforcement Learning—0
Robust Reinforcement Learning with Distributional Risk-averse formulation—0
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