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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 1401–1425 of 1918 papers

TitleStatusHype
Q-Learning in Regularized Mean-field Games—0
Distributed Reinforcement Learning for Cooperative Multi-Robot Object Manipulation—0
Deep Constrained Q-learning—0
Deep Reinforcement Learning with Weighted Q-Learning—0
Active Perception and Representation for Robotic Manipulation—0
Application of Deep Q-Network in Portfolio Management—0
A General Framework for Learning Mean-Field Games—0
Provably Efficient Model-Free Algorithm for MDPs with Peak Constraints—0
Indirect and Direct Training of Spiking Neural Networks for End-to-End Control of a Lane-Keeping Vehicle—0
Privacy-Cost Management in Smart Meters Using Deep Reinforcement Learning—0
Software-Level Accuracy Using Stochastic Computing With Charge-Trap-Flash Based Weight Matrix—0
A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles—0
Transfer Reinforcement Learning under Unobserved Contextual Information—0
Reinforcement Learning Based Cooperative Coded Caching under Dynamic Popularities in Ultra-Dense Networks—0
Relevance-Guided Modeling of Object Dynamics for Reinforcement Learning—0
Adaptive Structural Hyper-Parameter Configuration by Q-Learning—0
Contextual Policy Transfer in Reinforcement Learning Domains via Deep Mixtures-of-Experts—0
Deep Reinforcement Learning for FlipIt Security Game—0
ConQUR: Mitigating Delusional Bias in Deep Q-learningCode0
G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning—0
Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization—0
A Double Q-Learning Approach for Navigation of Aerial Vehicles with Connectivity Constraint—0
Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning—0
Q-learning with Uniformly Bounded Variance: Large Discounting is Not a Barrier to Fast Learning—0
Periodic Q-Learning—0
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