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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 1361–1370 of 1918 papers

TitleStatusHype
On Assessing The Safety of Reinforcement Learning algorithms Using Formal Methods—0
On Bellman's principle of optimality and Reinforcement learning for safety-constrained Markov decision process—0
On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collection—0
On Convergence of Average-Reward Off-Policy Control Algorithms in Weakly Communicating MDPs—0
On Convergence of Average-Reward Q-Learning in Weakly Communicating Markov Decision Processes—0
On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios—0
On Designing Multi-UAV aided Wireless Powered Dynamic Communication via Hierarchical Deep Reinforcement Learning—0
On Global Convergence Rates for Federated Policy Gradient under Heterogeneous Environment—0
On Information Asymmetry in Competitive Multi-Agent Reinforcement Learning: Convergence and Optimality—0
Online Adaptive Optimal Control Algorithm Based on Synchronous Integral Reinforcement Learning With Explorations—0
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