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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 1381–1390 of 1918 papers

TitleStatusHype
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
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