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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 591–600 of 1918 papers

TitleStatusHype
Correlated Deep Q-learning based Microgrid Energy Management—0
Correct-by-synthesis reinforcement learning with temporal logic constraints—0
A Q-Learning-based Approach for Distributed Beam Scheduling in mmWave Networks—0
A Finite-Time Analysis of Q-Learning with Neural Network Function Approximation—0
CoordiQ : Coordinated Q-learning for Electric Vehicle Charging Recommendation—0
Coordinating Ride-Pooling with Public Transit using Reward-Guided Conservative Q-Learning: An Offline Training and Online Fine-Tuning Reinforcement Learning Framework—0
A Q-learning approach to the continuous control problem of robot inverted pendulum balancing—0
Cooperative Reward Shaping for Multi-Agent Pathfinding—0
Cooperative Optimal Output Tracking for Discrete-Time Multiagent Systems: Stabilizing Policy Iteration Frameworks and Analysis—0
A Q-learning Approach for Adherence-Aware Recommendations—0
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