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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 371–380 of 1918 papers

Title	Date	Tasks	Status	Hype	Score
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning	Oct 27, 2019	Deep Reinforcement LearningImitation Learning	CodeCode Available	0	5
AlignIQL: Policy Alignment in Implicit Q-Learning through Constrained Optimization	May 28, 2024	D4RLOffline RL	CodeCode Available	0	5
Mixed-Integer Optimal Control via Reinforcement Learning: A Case Study on Hybrid Electric Vehicle Energy Management	May 2, 2023	continuous-controlContinuous Control	CodeCode Available	0	5
Model-Free Adaptive Optimal Control of Episodic Fixed-Horizon Manufacturing Processes using Reinforcement Learning	Sep 18, 2018	Model Predictive ControlQ-Learning	CodeCode Available	0	5
Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted Regression	May 28, 2024	Imitation LearningMuJoCo	CodeCode Available	0	5
Deep Q-learning from Demonstrations	Apr 12, 2017	Decision MakingDeep Reinforcement Learning	CodeCode Available	0	5
DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather Data	Oct 8, 2023	Autonomous DrivingQ-Learning	CodeCode Available	0	5
ConQUR: Mitigating Delusional Bias in Deep Q-learning	Feb 27, 2020	Atari GamesQ-Learning	CodeCode Available	0	5
Deep Quality-Value (DQV) Learning	Sep 30, 2018	Atari GamesDeep Reinforcement Learning	CodeCode Available	0	5
Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detection	Nov 27, 2021	Intrusion DetectionNetwork Intrusion Detection	CodeCode Available	0	5

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