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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 191–200 of 1918 papers

TitleStatusHype
Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement LearningCode0
Active exploration in parameterized reinforcement learningCode0
Diagnosing Bottlenecks in Deep Q-learning AlgorithmsCode0
DRL4AOI: A DRL Framework for Semantic-aware AOI Segmentation in Location-Based ServicesCode0
Efficient Collaborative Multi-Agent Deep Reinforcement Learning for Large-Scale Fleet ManagementCode0
Exploring reinforcement learning techniques for discrete and continuous control tasks in the MuJoCo environmentCode0
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNetCode0
Distributionally Robust Deep Q-LearningCode0
A Novel Update Mechanism for Q-Networks Based On Extreme Learning MachinesCode0
Deep-Q Learning with Hybrid Quantum Neural Network on Solving Maze ProblemsCode0
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