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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 31–40 of 137 papers

TitleStatusHype
Distributional Model Equivalence for Risk-Sensitive Reinforcement LearningCode0
Distributional constrained reinforcement learning for supply chain optimizationCode0
Distributional Bellman Operators over Mean EmbeddingsCode0
Distributional Reinforcement Learning with Regularized Wasserstein LossCode0
Distributional Reinforcement Learning for Energy-Based Sequential ModelsCode0
A Robust Quantile Huber Loss With Interpretable Parameter Adjustment In Distributional Reinforcement LearningCode0
Distributional Reinforcement Learning for Multi-Dimensional Reward FunctionsCode0
Distributional Reinforcement Learning with Quantile RegressionCode0
Estimation and Inference in Distributional Reinforcement LearningCode0
Estimating Risk and Uncertainty in Deep Reinforcement LearningCode0
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