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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 761–770 of 1918 papers

TitleStatusHype
A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning—0
Playing a 2D Game Indefinitely using NEAT and Reinforcement Learning—0
Structural Similarity for Improved Transfer in Reinforcement Learning—0
Finite-Time Analysis of Asynchronous Q-learning under Diminishing Step-Size from Control-Theoretic View—0
On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios—0
Multi-Source AoI-Constrained Resource Minimization under HARQ: Heterogeneous Sampling Processes—0
A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari GamesCode1
DDPG Learning for Aerial RIS-Assisted MU-MISO Communications—0
Approximate Nash Equilibrium Learning for n-Player Markov Games in Dynamic Pricing—0
Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement LearningCode1
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