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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 1221–1230 of 1918 papers

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Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction—0
Sample Complexity of Kernel-Based Q-Learning—0
Sample Complexity of Variance-reduced Distributionally Robust Q-learning—0
Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model—0
Sample Efficient Reinforcement Learning in Mixed Systems through Augmented Samples and Its Applications to Queueing Networks—0
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation—0
Sample-Optimal Parametric Q-Learning Using Linearly Additive Features—0
SAPO-RL: Sequential Actuator Placement Optimization for Fuselage Assembly via Reinforcement Learning—0
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation—0
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