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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 341–350 of 1918 papers

Title	Date	Tasks	Status	Hype	Score
Deep Q-learning from Demonstrations	Apr 12, 2017	Decision MakingDeep Reinforcement Learning	CodeCode Available	0	5
Implications of Decentralized Q-learning Resource Allocation in Wireless Networks	May 30, 2017	Q-LearningReinforcement Learning	CodeCode Available	0	5
Increasing the Action Gap: New Operators for Reinforcement Learning	Dec 15, 2015	Atari GamesQ-Learning	CodeCode Available	0	5
Information-Directed Exploration for Deep Reinforcement Learning	Dec 18, 2018	Atari GamesDeep Reinforcement Learning	CodeCode Available	0	5
Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic Environments	Oct 9, 2020	Incremental LearningQ-Learning	CodeCode Available	0	5
Policy Iterations for Reinforcement Learning Problems in Continuous Time and Space -- Fundamental Theory and Methods	May 9, 2017	Decision MakingQ-Learning	CodeCode Available	0	5
Balancing Value Underestimation and Overestimation with Realistic Actor-Critic	Oct 19, 2021	continuous-controlContinuous Control	CodeCode Available	0	5
A Deep Learning Approach to Grasping the Invisible	Sep 11, 2019	Deep LearningQ-Learning	CodeCode Available	0	5
Angrier Birds: Bayesian reinforcement learning	Jan 6, 2016	Efficient ExplorationQ-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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