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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 811–820 of 1918 papers

TitleStatusHype
GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization—0
Enhanced Q-Learning Approach to Finite-Time Reachability with Maximum Probability for Probabilistic Boolean Control Networks—0
Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery—0
Graph Exploration for Effective Multi-agent Q-Learning—0
Cellular traffic offloading via Opportunistic Networking with Reinforcement Learning—0
Graph Q-Learning for Combinatorial Optimization—0
Greedy-Step Off-Policy Reinforcement Learning—0
Greedy UnMixing for Q-Learning in Multi-Agent Reinforcement Learning—0
A new multilayer optical film optimal method based on deep q-learning—0
A Deep Reinforcement Learning Architecture for Multi-stage Optimal Control—0
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