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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 1291–1300 of 1918 papers

TitleStatusHype
Preventing Value Function Collapse in Ensemble Q-Learning by Maximizing Representation Diversity—0
Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments—0
Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret—0
Near-Optimal Reinforcement Learning with Self-Play—0
Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm—0
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode1
Parameterized MDPs and Reinforcement Learning Problems -- A Maximum Entropy Principle Based Framework—0
Semantic Visual Navigation by Watching YouTube VideosCode1
Q-learning with Logarithmic Regret—0
The Sample Complexity of Teaching-by-Reinforcement on Q-Learning—0
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