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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 51–60 of 137 papers

TitleStatusHype
Distributional Reinforcement Learning with Monotonic Splines—0
Controlling Synthetic Characters in Simulations: A Case for Cognitive Architectures and Sigma—0
Distributional Reinforcement Learning with Ensembles—0
Distributional Reinforcement Learning with Online Risk-awareness Adaption—0
Distributional reinforcement learning with linear function approximation—0
Conservative Distributional Reinforcement Learning with Safety Constraints—0
An Analysis of Quantile Temporal-Difference Learning—0
An Analysis of Categorical Distributional Reinforcement Learning—0
Distributional Reinforcement Learning on Path-dependent Options—0
Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds—0
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