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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 1311–1320 of 1918 papers

TitleStatusHype
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
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