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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 141–150 of 1918 papers

Title	Date	Tasks	Status	Hype
Addressing Function Approximation Error in Actor-Critic Methods	Feb 26, 2018	Continuous ControlOpenAI Gym	CodeCode Available	1
Mean Field Multi-Agent Reinforcement Learning	Feb 15, 2018	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor	Jan 4, 2018	Continuous ControlDecision Making	CodeCode Available	1
Automated Cloud Provisioning on AWS using Deep Reinforcement Learning	Sep 13, 2017	Cloud ComputingDeep Reinforcement Learning	CodeCode Available	1
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments	Jun 7, 2017	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available	1
Evolution Strategies as a Scalable Alternative to Reinforcement Learning	Mar 10, 2017	Atari GamesMuJoCo	CodeCode Available	1
Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning	Feb 28, 2017	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1
Continuous Deep Q-Learning with Model-based Acceleration	Mar 2, 2016	continuous-controlContinuous Control	CodeCode Available	1
Multiagent Cooperation and Competition with Deep Reinforcement Learning	Nov 27, 2015	Deep Reinforcement LearningQ-Learning	CodeCode Available	1
Deep Reinforcement Learning with Double Q-learning	Sep 22, 2015	Atari GamesDeep Reinforcement Learning	CodeCode Available	1

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