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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 21–30 of 137 papers

TitleStatusHype
Beyond Average Return in Markov Decision Processes—0
Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds—0
Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space—0
Conservative Distributional Reinforcement Learning with Safety Constraints—0
A Distributional Perspective on Actor-Critic Framework—0
Controlling Synthetic Characters in Simulations: A Case for Cognitive Architectures and Sigma—0
Cramer Type Distances for Learning Gaussian Mixture Models by Gradient Descent—0
An introduction to reinforcement learning for neuroscience—0
CTRLS: Chain-of-Thought Reasoning via Latent State-Transition—0
Deep Distributional Learning with Non-crossing Quantile Network—0
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