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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

Showing 16511675 of 1918 papers

TitleStatusHype
Active Deep Q-learning with Demonstration0
Revisiting the Softmax Bellman Operator: New Benefits and New PerspectiveCode0
Non-delusional Q-learning and value-iteration0
Urban Driving with Multi-Objective Deep Reinforcement LearningCode0
Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy LearningCode0
Reinforcement Learning with A* and a Deep HeuristicCode0
Emergence of Addictive Behaviors in Reinforcement Learning Agents0
Deep Q learning for fooling neural networksCode0
Managing App Install Ad Campaigns in RTB: A Q-Learning Approach0
Deep Reinforcement Learning via L-BFGS Optimization0
Deep Reinforcement Learning for Green Security Games with Real-Time Information0
Reinforcement Learning based Dynamic Model Selection for Short-Term Load Forecasting0
Double Q-PID algorithm for mobile robot controlCode0
Approximate Dynamic Oracle for Dependency Parsing with Reinforcement Learning0
Structure Learning of Deep Neural Networks with Q-Learning0
Distributive Dynamic Spectrum Access through Deep Reinforcement Learning: A Reservoir Computing Based Approach0
Multi-Agent Reinforcement Learning Based Resource Allocation for UAV Networks0
Learning Negotiating Behavior Between Cars in Intersections using Deep Q-Learning0
Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy ImprovementCode0
Optimization of Molecules via Deep Reinforcement LearningCode1
Finding the best design parameters for optical nanostructures using reinforcement learning0
Assessing the Potential of Classical Q-learning in General Game PlayingCode0
Learning to Sketch with Deep Q Networks and Demonstrated Strokes0
Learning to Reason0
Reinforcement Evolutionary Learning Method for self-learning0
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