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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 161–170 of 1918 papers

Title	Date	Tasks	Status	Hype	Score
Dynamic control of self-assembly of quasicrystalline structures through reinforcement learning	Sep 13, 2023	Q-Learningreinforcement-learning	CodeCode Available	0	5
Efficient Model-free Reinforcement Learning in Metric Spaces	May 1, 2019	Q-Learningreinforcement-learning	CodeCode Available	0	5
Evolution of cooperation in a bimodal mixture of conditional cooperators	Feb 11, 2025	Q-Learning	CodeCode Available	0	5
Distributionally Robust Deep Q-Learning	May 25, 2025	Q-Learning	CodeCode Available	0	5
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet	Dec 15, 2022	Deep Reinforcement LearningMulti-agent Reinforcement Learning	CodeCode Available	0	5
Double Q-PID algorithm for mobile robot control	Nov 1, 2018	Active LearningQ-Learning	CodeCode Available	0	5
Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement Learning	Sep 10, 2024	Deep Reinforcement LearningOpenAI Gym	CodeCode Available	0	5
Agent Performing Autonomous Stock Trading under Good and Bad Situations	Jun 6, 2023	Decision MakingDeep Reinforcement Learning	CodeCode Available	0	5
Adaptive Discretization for Episodic Reinforcement Learning in Metric Spaces	Oct 17, 2019	Q-Learningreinforcement-learning	CodeCode Available	0	5
Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction	Jun 3, 2019	continuous-controlContinuous Control	CodeCode Available	0	5

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