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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 351–360 of 1918 papers

TitleStatusHype
Caching Placement and Resource Allocation for Cache-Enabling UAV NOMA Networks—0
C-Learning: Learning to Achieve Goals via Recursive Classification—0
Cache-Aided NOMA Mobile Edge Computing: A Reinforcement Learning Approach—0
Collaborative Deep Reinforcement Learning for Joint Object Search—0
A Differentiable Physics Engine for Deep Learning in Robotics—0
Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear—0
An MDP Model for Censoring in Harvesting Sensors: Optimal and Approximated Solutions—0
Control-Tutored Reinforcement Learning: Towards the Integration of Data-Driven and Model-Based Control—0
Combining policy gradient and Q-learning—0
An Efficient and Uncertainty-aware Reinforcement Learning Framework for Quality Assurance in Extrusion Additive Manufacturing—0
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