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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 451–460 of 1918 papers

Title	Date	Tasks	Status	Hype	Score
Dynamic-Weighted Simplex Strategy for Learning Enabled Cyber Physical Systems	Feb 6, 2019	Autonomous DrivingQ-Learning	CodeCode Available	0	5
SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learning	Jan 6, 2024	Deep Reinforcement LearningDiversity	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
Deep Coordination Graphs	Sep 27, 2019	Multi-agent Reinforcement LearningQ-Learning	CodeCode Available	0	5
Hard Prompts Made Interpretable: Sparse Entropy Regularization for Prompt Tuning with RL	Jul 20, 2024	Few-Shot Text ClassificationQ-Learning	CodeCode Available	0	5
A Semantic-Aware Multiple Access Scheme for Distributed, Dynamic 6G-Based Applications	Jan 12, 2024	Decision MakingDeep Reinforcement Learning	CodeCode Available	0	5
OmniEcon Nexus: Global Microeconomic Simulation Engine	Apr 7, 2025	Data IntegrationPortfolio Optimization	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
ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations	Oct 26, 2019	Atari GamesDeep Reinforcement Learning	CodeCode Available	0	5

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