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 71–80 of 137 papers

TitleStatusHype
Distributional Reinforcement Learning for Risk-Sensitive Policies—0
Distributional Reinforcement Learning for mmWave Communications with Intelligent Reflectors on a UAV—0
Distributional Reinforcement Learning for Scheduling of Chemical Production Processes—0
Distributional Reinforcement Learning on Path-dependent Options—0
Distributional reinforcement learning with linear function approximation—0
Distributional Reinforcement Learning with Ensembles—0
Distributional Reinforcement Learning with Monotonic Splines—0
Distributional Reinforcement Learning with Dual Expectile-Quantile Regression—0
Distributional Reinforcement Learning with Online Risk-awareness Adaption—0
Diverse Projection Ensembles for Distributional Reinforcement Learning—0
Show:102550
← PrevPage 8 of 14Next →

No leaderboard results yet.