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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 1451–1460 of 1918 papers

TitleStatusHype
Quantum Observables for continuous control of the Quantum Approximate Optimization Algorithm via Reinforcement Learning—0
Efficient Drone Mobility Support Using Reinforcement Learning—0
Asymptotics of Reinforcement Learning with Neural Networks—0
Modelling Bahdanau Attention using Election methods aided by Q-Learning—0
Two-stage WECC Composite Load Modeling: A Double Deep Q-Learning Networks Approach—0
Challenging On Car Racing Problem from OpenAI gym—0
On Solving the 2-Dimensional Greedy Shooter Problem for UAVsCode0
Generalized Speedy Q-learningCode0
Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning—0
Biomimetic Ultra-Broadband Perfect Absorbers Optimised with Reinforcement Learning—0
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