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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 1491–1500 of 1918 papers

TitleStatusHype
Momentum-based Accelerated Q-learningCode0
Partially Detected Intelligent Traffic Signal Control: Environmental Adaptation—0
Policy Learning for Malaria ControlCode0
Reverse Experience Replay—0
Automatic Data Augmentation by Learning the Deterministic PolicyCode0
Adaptive Discretization for Episodic Reinforcement Learning in Metric SpacesCode0
SEED RL: Scalable and Efficient Deep-RL with Accelerated Central InferenceCode0
On the Reduction of Variance and Overestimation of Deep Q-Learning—0
Zap Q-Learning With Nonlinear Function Approximation—0
Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments—0
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