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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 1376–1400 of 1918 papers

TitleStatusHype
Transfer Reinforcement Learning under Unobserved Contextual Information—0
Tuning Path Tracking Controllers for Autonomous Cars Using Reinforcement Learning—0
Two Phase Q-learning for Bidding-based Vehicle Sharing—0
Two-stage WECC Composite Load Modeling: A Double Deep Q-Learning Networks Approach—0
Two-Step Q-Learning—0
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
Understanding Hindsight Goal Relabeling from a Divergence Minimization Perspective—0
Understanding Reinforcement Learning Algorithms: The Progress from Basic Q-learning to Proximal Policy Optimization—0
Understanding the theoretical properties of projected Bellman equation, linear Q-learning, and approximate value iteration—0
Unified continuous-time q-learning for mean-field game and mean-field control problems—0
Unified ODE Analysis of Smooth Q-Learning Algorithms—0
Unified Reinforcement Q-Learning for Mean Field Game and Control Problems—0
Unifying Ensemble Methods for Q-learning via Social Choice Theory—0
Unifying Top-down and Bottom-up for Recurrent Visual Attention—0
Universal Approximation Theorem for Deep Q-Learning via FBSDE System—0
Universal Approximation Theorem of Deep Q-Networks—0
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