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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 571–580 of 1918 papers

TitleStatusHype
Bayesian Risk-Averse Q-Learning with Streaming Observations—0
The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond—0
Model-Free Robust Average-Reward Reinforcement Learning—0
Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning—0
Mastering Percolation-like Games with Deep LearningCode0
On Practical Robust Reinforcement Learning: Practical Uncertainty Set and Double-Agent Algorithm—0
Deep Q-Learning-based Distribution Network Reconfiguration for Reliability Improvement—0
Mixed-Integer Optimal Control via Reinforcement Learning: A Case Study on Hybrid Electric Vehicle Energy ManagementCode0
Model-free Motion Planning of Autonomous Agents for Complex Tasks in Partially Observable EnvironmentsCode0
BCQQ: Batch-Constraint Quantum Q-Learning with Cyclic Data Re-uploading—0
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