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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 1011–1020 of 1918 papers

TitleStatusHype
Provably Efficient Multi-Agent Reinforcement Learning with Fully Decentralized Communication—0
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle—0
Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle—0
Provably Efficient Q-Learning with Low Switching Cost—0
Provably Efficient Reinforcement Learning with Aggregated States—0
Provably Efficient Reinforcement Learning in Decentralized General-Sum Markov Games—0
Provably More Efficient Q-Learning in the One-Sided-Feedback/Full-Feedback Settings—0
Direct Data-Driven Discrete-time Bilinear Biquadratic Regulator—0
Pruning the Way to Reliable Policies: A Multi-Objective Deep Q-Learning Approach to Critical Care—0
Pseudorehearsal in value function approximation—0
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