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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 1741–1750 of 1918 papers

TitleStatusHype
Variation-resistant Q-learning: Controlling and Utilizing Estimation Bias in Reinforcement Learning for Better PerformanceCode0
Parallel Q-Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel SimulationCode0
Parameter-free Reduction of the Estimation Bias in Deep Reinforcement Learning for Deterministic Policy GradientsCode0
RadDQN: a Deep Q Learning-based Architecture for Finding Time-efficient Minimum Radiation Exposure PathwayCode0
Boosting Soft Q-Learning by BoundingCode0
Automaton-Guided Curriculum Generation for Reinforcement Learning AgentsCode0
Variations on the Reinforcement Learning performance of BlackjackCode0
Model-Free Adaptive Optimal Control of Episodic Fixed-Horizon Manufacturing Processes using Reinforcement LearningCode0
Deterministic Implementations for Reproducibility in Deep Reinforcement LearningCode0
Designing Neural Network Architectures using Reinforcement LearningCode0
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