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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 441–450 of 1918 papers

Title	Date	Tasks	Status	Hype	Score
SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference	Oct 15, 2019	Q-LearningReinforcement Learning	CodeCode Available	0	5
Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution	Jul 2, 2015	Q-LearningSelf-Learning	CodeCode Available	0	5
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings	Oct 29, 2020	Change Point DetectionOff-policy evaluation	CodeCode Available	0	5
Decoding fairness: a reinforcement learning perspective	Dec 20, 2024	FairnessImitation Learning	CodeCode Available	0	5
Deep Active Inference for Pixel-Based Discrete Control: Evaluation on the Car Racing Problem	Sep 9, 2021	Car RacingQ-Learning	CodeCode Available	0	5
Dynamic-Weighted Simplex Strategy for Learning Enabled Cyber Physical Systems	Feb 6, 2019	Autonomous DrivingQ-Learning	CodeCode Available	0	5
Augmented Q Imitation Learning (AQIL)	Mar 31, 2020	Deep Reinforcement LearningImitation Learning	CodeCode Available	0	5
Decision Making in Non-Stationary Environments with Policy-Augmented Search	Jan 6, 2024	Decision MakingDecision Making Under Uncertainty	CodeCode Available	0	5
Solving reward-collecting problems with UAVs: a comparison of online optimization and Q-learning	Nov 30, 2021	Autonomous VehiclesQ-Learning	CodeCode Available	0	5
Deep Coordination Graphs	Sep 27, 2019	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	0	5

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