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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

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Showing 1381–1390 of 1918 papers

TitleStatusHype
Two Timescale Convergent Q-learning for Sleep--Scheduling in Wireless Sensor Networks—0
Two-Timescale Networks for Nonlinear Value Function Approximation—0
Two-Timescale Q-Learning with Function Approximation in Zero-Sum Stochastic Games—0
UAV Aided Search and Rescue Operation Using Reinforcement Learning—0
UAV-Assisted Space-Air-Ground Integrated Networks: A Technical Review of Recent Learning Algorithms—0
UAV Base Station Trajectory Optimization Based on Reinforcement Learning in Post-disaster Search and Rescue Operations—0
UAV Swarm Deployment and Trajectory for 3D Area Coverage via Reinforcement Learning—0
UCB Exploration via Q-Ensembles—0
Unbiased Methods for Multi-Goal Reinforcement Learning—0
Uncertainty Weighted Offline Reinforcement Learning—0
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