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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 1301–1325 of 1918 papers

TitleStatusHype
Runtime Adaptation in Wireless Sensor Nodes Using Structured Learning—0
Self-Imitation Learning via Generalized Lower Bound Q-learning—0
Safety-guaranteed Reinforcement Learning based on Multi-class Support Vector Machine—0
Human and Multi-Agent collaboration in a human-MARL teaming framework—0
Deep Reinforcement Learning for Neural Control—0
Decorrelated Double Q-learning—0
Exploration by Maximizing Rényi Entropy for Reward-Free RL Framework—0
Zeroth-Order Supervised Policy Improvement—0
Q-greedyUCB: a New Exploration Policy for Adaptive and Resource-efficient Scheduling—0
Privacy-Cost Management in Smart Meters with Mutual Information-Based Reinforcement Learning—0
Multi-Agent Reinforcement Learning in a Realistic Limit Order Book Market Simulation—0
Model-Free Algorithm and Regret Analysis for MDPs with Long-Term Constraints—0
Fitted Q-Learning for Relational Domains—0
Self-Supervised Reinforcement Learning for Recommender Systems—0
Reinforcement Learning-Based Joint Self-Optimisation Method for the Fuzzy Logic Handover Algorithm in 5G HetNets—0
Balancing a CartPole System with Reinforcement Learning -- A Tutorial—0
A Model-free Learning Algorithm for Infinite-horizon Average-reward MDPs with Near-optimal Regret—0
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory—0
Conservative Q-Learning for Offline Reinforcement LearningCode1
A Multi-step and Resilient Predictive Q-learning Algorithm for IoT with Human Operators in the Loop: A Case Study in Water Supply Networks—0
Logical Team Q-learning: An approach towards factored policies in cooperative MARL—0
Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction—0
A Novel Update Mechanism for Q-Networks Based On Extreme Learning MachinesCode0
Multi-Agent Determinantal Q-LearningCode1
Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning—0
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