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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 1351–1400 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
Online Robust Reinforcement Learning with Model Uncertainty—0
Online Statistical Inference for Nonlinear Stochastic Approximation with Markovian Data—0
Asymptotic Analysis of Sample-averaged Q-learning—0
Online Target Q-learning with Reverse Experience Replay: Efficiently finding the Optimal Policy for Linear MDPs—0
Online Transfer Learning in Reinforcement Learning Domains—0
Online waveform selection for cognitive radar—0
On optimal tracking portfolio in incomplete markets: The reinforcement learning approach—0
On Practical Robust Reinforcement Learning: Practical Uncertainty Set and Double-Agent Algorithm—0
On the Convergence and Sample Complexity Analysis of Deep Q-Networks with ε-Greedy Exploration—0
On the Convergence of Approximate and Regularized Policy Iteration Schemes—0
On the Convergence of Monte Carlo UCB for Random-Length Episodic MDPs—0
On the Convergence Rates of Federated Q-Learning across Heterogeneous Environments—0
On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization—0
On the Reduction of Variance and Overestimation of Deep Q-Learning—0
OPA-Pack: Object-Property-Aware Robotic Bin Packing—0
OpenSense: An Open-World Sensing Framework for Incremental Learning and Dynamic Sensor Scheduling on Embedded Edge Devices—0
Operator Deep Q-Learning: Zero-Shot Reward Transferring in Reinforcement Learning—0
Optimal and Fast Real-time Resources Slicing with Deep Dueling Neural Networks—0
Optimal Beam Association for High Mobility mmWave Vehicular Networks: Lightweight Parallel Reinforcement Learning Approach—0
Optimal Control of District Cooling Energy Plant with Reinforcement Learning and MPC—0
Optimal coordination of resources: A solution from reinforcement learning—0
Optimal Cycling of a Heterogenous Battery Bank via Reinforcement Learning—0
Optimal Decision-Making in Mixed-Agent Partially Observable Stochastic Environments via Reinforcement Learning—0
Optimal Demand Response Using Device Based Reinforcement Learning—0
Optimal Design and Implementation of an Open-source Emulation Platform for User-Centric Shared E-mobility Services—0
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