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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 1376–1400 of 1918 papers

TitleStatusHype
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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