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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 41–50 of 137 papers

TitleStatusHype
Pitfall of Optimism: Distributional Reinforcement Learning by Randomizing Risk Criterion—0
Distributional Reinforcement Learning with Online Risk-awareness Adaption—0
Estimation and Inference in Distributional Reinforcement LearningCode0
Learning Risk-Aware Quadrupedal Locomotion using Distributional Reinforcement Learning—0
Deep Reinforcement Learning for Artificial Upwelling Energy Management—0
Value-Distributional Model-Based Reinforcement LearningCode0
Variance Control for Distributional Reinforcement LearningCode0
Cramer Type Distances for Learning Gaussian Mixture Models by Gradient Descent—0
Distributional Model Equivalence for Risk-Sensitive Reinforcement LearningCode0
Is Risk-Sensitive Reinforcement Learning Properly Resolved?—0
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