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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 821–830 of 1918 papers

TitleStatusHype
Growing Q-Networks: Solving Continuous Control Tasks with Adaptive Control Resolution—0
Guiding Reinforcement Learning Exploration Using Natural Language—0
Deep SIMBAD: Active Landmark-based Self-localization Using Ranking -based Scene Descriptor—0
Hamilton-Jacobi-Bellman Equations for Q-Learning in Continuous Time—0
A Lifetime Extended Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles via Self-Learning Fuzzy Reinforcement Learning—0
Convert Language Model into a Value-based Strategic Planner—0
Harnessing Deep Q-Learning for Enhanced Statistical Arbitrage in High-Frequency Trading: A Comprehensive Exploration—0
Deep Robot Sketching: An application of Deep Q-Learning Networks for human-like sketching—0
HAVER: Instance-Dependent Error Bounds for Maximum Mean Estimation and Applications to Q-Learning and Monte Carlo Tree Search—0
A Conflicts-free, Speed-lossless KAN-based Reinforcement Learning Decision System for Interactive Driving in Roundabouts—0
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