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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
A Local Temporal Difference Code for Distributional Reinforcement Learning—0
An Analysis of Categorical Distributional Reinforcement Learning—0
An Analysis of Quantile Temporal-Difference Learning—0
An introduction to reinforcement learning for neuroscience—0
A Point-Based Algorithm for Distributional Reinforcement Learning in Partially Observable Domains—0
Automatic Risk Adaptation in Distributional Reinforcement Learning—0
Batch-Constrained Distributional Reinforcement Learning for Session-based Recommendation—0
Bayesian Distributional Policy Gradients—0
Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space—0
Beyond Average Return in Markov Decision Processes—0
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