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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 1791–1800 of 1918 papers

TitleStatusHype
Credit Assignment: Challenges and Opportunities in Developing Human-like AI Agents—0
Credit-cognisant reinforcement learning for multi-agent cooperation—0
Criticality-Based Varying Step-Number Algorithm for Reinforcement Learning—0
Cross Learning in Deep Q-Networks—0
Curriculum Q-Learning for Visual Vocabulary Acquisition—0
Cycles and collusion in congestion games under Q-learning—0
DASA: Delay-Adaptive Multi-Agent Stochastic Approximation—0
Data-Based Efficient Off-Policy Stabilizing Optimal Control Algorithms for Discrete-Time Linear Systems via Damping Coefficients—0
Data-Driven H-infinity Control with a Real-Time and Efficient Reinforcement Learning Algorithm: An Application to Autonomous Mobility-on-Demand Systems—0
Data-driven inventory management for new products: An adjusted Dyna-Q approach with transfer learning—0
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