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 11–20 of 137 papers

TitleStatusHype
Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement LearningCode1
An introduction to reinforcement learning for neuroscience—0
Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation—0
A Point-Based Algorithm for Distributional Reinforcement Learning in Partially Observable Domains—0
An Analysis of Quantile Temporal-Difference Learning—0
An Analysis of Categorical Distributional Reinforcement Learning—0
Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds—0
A Local Temporal Difference Code for Distributional Reinforcement Learning—0
Bellman Diffusion: Generative Modeling as Learning a Linear Operator in the Distribution Space—0
A Distributional Perspective on Actor-Critic Framework—0
Show:102550
← PrevPage 2 of 14Next →

No leaderboard results yet.