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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 811–820 of 1918 papers

TitleStatusHype
Exploratory Control with Tsallis Entropy for Latent Factor Models—0
On the Global Convergence of Fitted Q-Iteration with Two-layer Neural Network Parametrization—0
Reinforcement Learning in Non-Markovian Environments—0
Offline RL With Realistic Datasets: Heteroskedasticity and Support Constraints—0
DynamicLight: Two-Stage Dynamic Traffic Signal TimingCode0
Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systems—0
Quantum deep recurrent reinforcement learning—0
Attitude Control of Highly Maneuverable Aircraft Using an Improved Q-learning—0
Sufficient Exploration for Convex Q-learning—0
Model-Free Characterizations of the Hamilton-Jacobi-Bellman Equation and Convex Q-Learning in Continuous Time—0
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