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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 1376–1400 of 1918 papers

TitleStatusHype
A General Framework for Learning Mean-Field Games—0
Provably Efficient Model-Free Algorithm for MDPs with Peak Constraints—0
Privacy-Cost Management in Smart Meters Using Deep Reinforcement Learning—0
Indirect and Direct Training of Spiking Neural Networks for End-to-End Control of a Lane-Keeping Vehicle—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
Optimistic Exploration even with a Pessimistic InitialisationCode1
Simultaneously Evolving Deep Reinforcement Learning Models using Multifactorial Optimization—0
G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning—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
Anypath Routing Protocol Design via Q-Learning for Underwater Sensor Networks—0
UAV Aided Search and Rescue Operation Using Reinforcement Learning—0
Agnostic Q-learning with Function Approximation in Deterministic Systems: Tight Bounds on Approximation Error and Sample Complexity—0
Maxmin Q-learning: Controlling the Estimation Bias of Q-learningCode1
Listwise Learning to Rank with Deep Q-Networks—0
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