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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 1871–1880 of 1918 papers

TitleStatusHype
Synthesis of Temporally-Robust Policies for Signal Temporal Logic Tasks using Reinforcement LearningCode0
Inverse Q-Learning Done Right: Offline Imitation Learning in Q^π-Realizable MDPsCode0
SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous RobotsCode0
Solving reward-collecting problems with UAVs: a comparison of online optimization and Q-learningCode0
Solving The Lunar Lander Problem under Uncertainty using Reinforcement LearningCode0
Investigating the Performance and Reliability, of the Q-Learning Algorithm in Various Unknown EnvironmentsCode0
Neural Temporal-Difference and Q-Learning Provably Converge to Global OptimaCode0
I Open at the Close: A Deep Reinforcement Learning Evaluation of Open Streets InitiativesCode0
Assessing the Potential of Classical Q-learning in General Game PlayingCode0
Deep Reinforcement Learning for Multi-class Imbalanced TrainingCode0
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