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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 121–130 of 1918 papers

Title	Date	Tasks	Status	Hype
Robust Deep Reinforcement Learning through Adversarial Loss	Aug 5, 2020	Adversarial AttackAtari Games	CodeCode Available	1
Robust Multi-Agent Reinforcement Learning with State Uncertainty	Jul 30, 2023	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	1
Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning	Jun 7, 2021	Multi-agent Reinforcement LearningOffline RL	CodeCode Available	1
Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration	Jun 20, 2022	Atari GamesDeep Reinforcement Learning	CodeCode Available	1
Semantic Visual Navigation by Watching YouTube Videos	Jun 17, 2020	Q-LearningVisual Navigation	CodeCode Available	1
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning	May 31, 2021	FairnessMulti-agent Reinforcement Learning	CodeCode Available	1
Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the Past	Jun 10, 2019	Deep Reinforcement LearningMuJoCo	CodeCode Available	1
Conservative Q-Learning for Offline Reinforcement Learning	Jun 8, 2020	continuous-controlContinuous Control	CodeCode Available	1
Automated Cloud Provisioning on AWS using Deep Reinforcement Learning	Sep 13, 2017	Cloud ComputingDeep Reinforcement Learning	CodeCode Available	1
MADiff: Offline Multi-agent Learning with Diffusion Models	May 27, 2023	Offline RLQ-Learning	CodeCode Available	1

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