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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 461–470 of 1918 papers

TitleStatusHype
Integrated Freeway Traffic Control Using Q-Learning with Adjacent Arterial Traffic Considerations—0
Reinforcement learning based local path planning for mobile robot—0
On the Convergence and Sample Complexity Analysis of Deep Q-Networks with ε-Greedy Exploration—0
AI on the Water: Applying DRL to Autonomous Vessel Navigation—0
Deep Reinforcement Learning-based Intelligent Traffic Signal Controls with Optimized CO2 emissionsCode1
Towards Robust Offline Reinforcement Learning under Diverse Data CorruptionCode1
Bad Values but Good Behavior: Learning Highly Misspecified Bandits and MDPs—0
Learning RL-Policies for Joint Beamforming Without Exploration: A Batch Constrained Off-Policy ApproachCode0
Integrated Sensing and Communication Neighbor Discovery for MANET with Gossip Mechanism—0
Boosting Continuous Control with Consistency PolicyCode1
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