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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 1371–1380 of 1918 papers

TitleStatusHype
Deep Reinforcement Learning with Weighted Q-Learning—0
DisCor: Corrective Feedback in Reinforcement Learning via Distribution CorrectionCode1
Active Perception and Representation for Robotic Manipulation—0
FACMAC: Factored Multi-Agent Centralised Policy GradientsCode1
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
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
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