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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
MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning—0
Automatic Risk Adaptation in Distributional Reinforcement Learning—0
Non-decreasing Quantile Function Network with Efficient Exploration for Distributional Reinforcement Learning—0
Bayesian Distributional Policy Gradients—0
Safe Distributional Reinforcement Learning—0
SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning—0
Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning—0
Controlling Synthetic Characters in Simulations: A Case for Cognitive Architectures and Sigma—0
Distributional Reinforcement Learning for Risk-Sensitive Policies—0
A Distributional Perspective on Actor-Critic Framework—0
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