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 561–570 of 1918 papers

TitleStatusHype
A Q-learning approach to the continuous control problem of robot inverted pendulum balancing—0
Algorithmic collusion under competitive design—0
Provable Reinforcement Learning for Networked Control Systems with Stochastic Packet Disordering—0
Data-efficient Deep Reinforcement Learning for Vehicle Trajectory Control—0
Anomaly Detection via Learning-Based Sequential Controlled Sensing—0
OpenSense: An Open-World Sensing Framework for Incremental Learning and Dynamic Sensor Scheduling on Embedded Edge Devices—0
Q-learning Based Optimal False Data Injection Attack on Probabilistic Boolean Control Networks—0
Reinforcement Learning from Diffusion Feedback: Q* for Image Search—0
A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation—0
FRAC-Q-Learning: A Reinforcement Learning with Boredom Avoidance Processes for Social Robots—0
Show:102550
← PrevPage 57 of 192Next →

No leaderboard results yet.