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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 1171–1180 of 1918 papers

TitleStatusHype
A Discrete-Time Switching System Analysis of Q-learning—0
Cooperation and Reputation Dynamics with Reinforcement Learning—0
Reversible Action Design for Combinatorial Optimization with Reinforcement Learning—0
Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis—0
Hedging of Financial Derivative Contracts via Monte Carlo Tree Search—0
Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States—0
Model-Augmented Q-learning—0
Revisiting Prioritized Experience Replay: A Value PerspectiveCode0
Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning—0
A review of motion planning algorithms for intelligent robotics—0
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