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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 1541–1550 of 1918 papers

TitleStatusHype
Q-DATA: Enhanced Traffic Flow Monitoring in Software-Defined Networks applying Q-learning—0
Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithmsCode0
Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity—0
Networked Control of Nonlinear Systems under Partial Observation Using Continuous Deep Q-Learning—0
STMARL: A Spatio-Temporal Multi-Agent Reinforcement Learning Approach for Cooperative Traffic Light Control—0
Deep Reinforcement Learning for Foreign Exchange Trading—0
Performing Deep Recurrent Double Q-Learning for Atari GamesCode0
Learn How to Cook a New Recipe in a New House: Using Map Familiarization, Curriculum Learning, and Bandit Feedback to Learn Families of Text-Based Adventure GamesCode0
Large-Scale Traffic Signal Control Using a Novel Multi-Agent Reinforcement Learning—0
Batch Recurrent Q-Learning for Backchannel Generation Towards Engaging Agents—0
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