SOTAVerified

Q-Learning

The goal of Q-learning is to learn a policy, which tells an agent what action to take under what circumstances.

( Image credit: Playing Atari with Deep Reinforcement Learning )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1071–1080 of 1918 papers

TitleStatusHype
Robotic Surgery With Lean Reinforcement LearningCode0
Action Candidate Based Clipped Double Q-learning for Discrete and Continuous Action TasksCode0
CARL-DTN: Context Adaptive Reinforcement Learning based Routing Algorithm in Delay Tolerant Network—0
RP-DQN: An application of Q-Learning to Vehicle Routing Problems—0
Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks—0
Reinforcement Learning for Traffic Signal Control: Comparison with Commercial Systems—0
A Simulated Experiment to Explore Robotic Dialogue Strategies for People with Dementia—0
Low-rank State-action Value-function ApproximationCode0
Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills—0
Prospect-theoretic Q-learning—0
Show:102550
← PrevPage 108 of 192Next →

No leaderboard results yet.