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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 1676–1700 of 1918 papers

TitleStatusHype
Managing App Install Ad Campaigns in RTB: A Q-Learning Approach—0
Deep Reinforcement Learning for Green Security Games with Real-Time Information—0
Deep Reinforcement Learning via L-BFGS Optimization—0
Reinforcement Learning based Dynamic Model Selection for Short-Term Load Forecasting—0
Approximate Dynamic Oracle for Dependency Parsing with Reinforcement Learning—0
Double Q-PID algorithm for mobile robot controlCode0
Structure Learning of Deep Neural Networks with Q-Learning—0
Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach—0
Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks—0
Learning Negotiating Behavior Between Cars in Intersections using Deep Q-Learning—0
Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy ImprovementCode0
Finding the best design parameters for optical nanostructures using reinforcement learning—0
Assessing the Potential of Classical Q-learning in General Game PlayingCode0
Learning to Sketch with Deep Q Networks and Demonstrated Strokes—0
Learning to Reason—0
Reinforcement Evolutionary Learning Method for self-learning—0
Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural NetworksCode0
Deep Quality-Value (DQV) LearningCode0
Reinforcement Learning in R—0
Accelerated Value Iteration via Anderson Mixing—0
A Convergent Variant of the Boltzmann Softmax Operator in Reinforcement Learning—0
Hybrid Policies Using Inverse Rewards for Reinforcement Learning—0
Convergent Reinforcement Learning with Function Approximation: A Bilevel Optimization Perspective—0
What Would pi* Do?: Imitation Learning via Off-Policy Reinforcement Learning—0
The wisdom of the crowd: reliable deep reinforcement learning through ensembles of Q-functions—0
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