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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 1351–1375 of 1918 papers

TitleStatusHype
Offline Guarded Safe Reinforcement Learning for Medical Treatment Optimization Strategies—0
Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes—0
Offline Reinforcement Learning and Sequence Modeling for Downlink Link Adaptation—0
Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets—0
Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient—0
Offline Reinforcement Learning with Imbalanced Datasets—0
Offline RL With Realistic Datasets: Heteroskedasticity and Support Constraints—0
Offline Robot Reinforcement Learning with Uncertainty-Guided Human Expert Sampling—0
Composite Q-learning: Multi-scale Q-function Decomposition and Separable Optimization—0
Off-policy Multi-step Q-learning—0
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
Online Antenna Tuning in Heterogeneous Cellular Networks with Deep Reinforcement Learning—0
On-line Building Energy Optimization using Deep Reinforcement Learning—0
Online Frequency Scheduling by Learning Parallel Actions—0
Online inductive learning from answer sets for efficient reinforcement learning exploration—0
Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing—0
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