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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 421–430 of 1918 papers

Title	Date	Tasks	Status	Hype	Score
Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detection	Nov 27, 2021	Intrusion DetectionNetwork Intrusion Detection	CodeCode Available	0	5
Reinforcement-Learning based routing for packet-optical networks with hybrid telemetry	Jun 18, 2024	Q-Learning	CodeCode Available	0	5
Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement	Oct 22, 2018	Policy Gradient MethodsQ-Learning	CodeCode Available	0	5
Reinforcement Learning with A* and a Deep Heuristic	Nov 19, 2018	Q-Learningreinforcement-learning	CodeCode Available	0	5
Reinforcement Learning with Dynamic Boltzmann Softmax Updates	Mar 14, 2019	Atari GamesQ-Learning	CodeCode Available	0	5
Automatic Data Augmentation by Learning the Deterministic Policy	Oct 18, 2019	Data AugmentationDeep Reinforcement Learning	CodeCode Available	0	5
A Kernel Loss for Solving the Bellman Equation	May 25, 2019	Q-LearningReinforcement Learning	CodeCode Available	0	5
Automata Learning meets Shielding	Dec 4, 2022	Q-LearningReinforcement Learning (RL)	CodeCode Available	0	5
Adaptive Symmetric Reward Noising for Reinforcement Learning	May 24, 2019	Autonomous DrivingQ-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

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