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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 1531–1540 of 1918 papers

TitleStatusHype
A Deep Learning Approach to Grasping the InvisibleCode0
Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning—0
A Multistep Lyapunov Approach for Finite-Time Analysis of Biased Stochastic Approximation—0
Q-Learning Based Aerial Base Station Placement for Fairness Enhancement in Mobile Networks—0
Fixed-Horizon Temporal Difference Methods for Stable Reinforcement Learning—0
Self-driving scale car trained by Deep reinforcement learning—0
Multi Pseudo Q-learning Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles—0
Deep Reinforcement Learning for Control of Probabilistic Boolean NetworksCode0
Encoders and Decoders for Quantum Expander Codes Using Machine Learning—0
Gradient Q(σ, λ): A Unified Algorithm with Function Approximation for Reinforcement Learning—0
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