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 76–100 of 137 papers

TitleStatusHype
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
Exploration by Distributional Reinforcement Learning—0
Exploration with Multi-Sample Target Values for Distributional Reinforcement Learning—0
Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations—0
A Finite Sample Analysis of Distributional TD Learning with Linear Function Approximation—0
Flow Models for Unbounded and Geometry-Aware Distributional Reinforcement Learning—0
Foundations of Multivariate Distributional Reinforcement Learning—0
GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing—0
A Simulation Environment and Reinforcement Learning Method for Waste Reduction—0
Hedging and Pricing Structured Products Featuring Multiple Underlying Assets—0
How Does Return Distribution in Distributional Reinforcement Learning Help Optimization?—0
Improving Robustness via Risk Averse Distributional Reinforcement Learning—0
Improving the generalizability and robustness of large-scale traffic signal control—0
Interpretable Stochastic Model Predictive Control using Distributional Reinforced Estimation for Quadrotor Tracking Systems—0
Invariance to Quantile Selection in Distributional Continuous Control—0
Is Risk-Sensitive Reinforcement Learning Properly Resolved?—0
Learning Risk-Aware Quadrupedal Locomotion using Distributional Reinforcement Learning—0
Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning—0
Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning—0
MMD-MIX: Value Function Factorisation with Maximum Mean Discrepancy for Cooperative Multi-Agent Reinforcement Learning—0
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning—0
Show:102550
← PrevPage 4 of 6Next →

No leaderboard results yet.