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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 1261–1270 of 1918 papers

TitleStatusHype
Language Inference with Multi-head Automata through Reinforcement Learning—0
Learning Dexterous Manipulation from Suboptimal Experts—0
A Nesterov's Accelerated quasi-Newton method for Global Routing using Deep Reinforcement Learning—0
Model-Based Reinforcement Learning for Type 1Diabetes Blood Glucose Control—0
Parameterized Reinforcement Learning for Optical System Optimization—0
Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic EnvironmentsCode0
Fictitious play in zero-sum stochastic games—0
Model-Free Non-Stationary RL: Near-Optimal Regret and Applications in Multi-Agent RL and Inventory Control—0
Machine Learning Empowered Trajectory and Passive Beamforming Design in UAV-RIS Wireless Networks—0
Finite-Time Analysis for Double Q-learning—0
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