SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–60 of 137 papers

TitleStatusHype
A Comparative Analysis of Expected and Distributional Reinforcement Learning—0
Demand-Side Scheduling Based on Multi-Agent Deep Actor-Critic Learning for Smart Grids—0
Distributional Reinforcement Learning with Dual Expectile-Quantile Regression—0
Distributional Reinforcement Learning with Online Risk-awareness Adaption—0
Deep Reinforcement Learning for Artificial Upwelling Energy Management—0
Cramer Type Distances for Learning Gaussian Mixture Models by Gradient Descent—0
Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning—0
Exploration by Distributional Reinforcement Learning—0
CTRLS: Chain-of-Thought Reasoning via Latent State-Transition—0
A Finite Sample Analysis of Distributional TD Learning with Linear Function Approximation—0
Show:102550
← PrevPage 6 of 14Next →

No leaderboard results yet.