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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 851–860 of 1918 papers

TitleStatusHype
Hyperparameter Optimization for Tracking With Continuous Deep Q-Learning—0
HyperQ-Opt: Q-learning for Hyperparameter Optimization—0
Energy-Efficient Power Allocation and Q-Learning-Based Relay Selection for Relay-Aided D2D Communication—0
A new convergent variant of Q-learning with linear function approximation—0
Energy Consumption and Battery Aging Minimization Using a Q-learning Strategy for a Battery/Ultracapacitor Electric Vehicle—0
Causal Mean Field Multi-Agent Reinforcement Learning—0
Imagination-Limited Q-Learning for Offline Reinforcement Learning—0
Imitating Language via Scalable Inverse Reinforcement Learning—0
Implementing Inductive bias for different navigation tasks through diverse RNN attrractors—0
Energy-aware optimization of UAV base stations placement via decentralized multi-agent Q-learning—0
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