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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 1271–1280 of 1918 papers

TitleStatusHype
Soft Q Network—0
Software-Level Accuracy Using Stochastic Computing With Charge-Trap-Flash Based Weight Matrix—0
SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems—0
A Generalized Minimax Q-learning Algorithm for Two-Player Zero-Sum Stochastic Games—0
Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity—0
Solving optimal stopping problems with Deep Q-Learning—0
Solving the Model Unavailable MARE using Q-Learning Algorithm—0
Solving the single-track train scheduling problem via Deep Reinforcement Learning—0
Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization—0
Toward Packet Routing with Fully-distributed Multi-agent Deep Reinforcement Learning—0
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