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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

Showing 251275 of 1918 papers

TitleStatusHype
Efficient Model-free Reinforcement Learning in Metric SpacesCode0
Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement LearningCode0
A Multi-Step Minimax Q-learning Algorithm for Two-Player Zero-Sum Markov GamesCode0
Audio-Driven Reinforcement Learning for Head-Orientation in Naturalistic EnvironmentsCode0
DRL4AOI: A DRL Framework for Semantic-aware AOI Segmentation in Location-Based ServicesCode0
Distributionally Robust Deep Q-LearningCode0
A disembodied developmental robotic agent called Samu BátfaiCode0
Double Q-PID algorithm for mobile robot controlCode0
An intelligent financial portfolio trading strategy using deep Q-learningCode0
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNetCode0
Dual Ensembled Multiagent Q-Learning with Hypernet RegularizerCode0
Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic MethodsCode0
Angrier Birds: Bayesian reinforcement learningCode0
Diagnosing Bottlenecks in Deep Q-learning AlgorithmsCode0
Designing Neural Network Architectures using Reinforcement LearningCode0
Active inference: demystified and comparedCode0
Deterministic Implementations for Reproducibility in Deep Reinforcement LearningCode0
DeepTPI: Test Point Insertion with Deep Reinforcement LearningCode0
Deep-Q Learning with Hybrid Quantum Neural Network on Solving Maze ProblemsCode0
Action Candidate Driven Clipped Double Q-learning for Discrete and Continuous Action TasksCode0
Deep Reinforcement Learning with a Natural Language Action SpaceCode0
DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic NavigationCode0
Deep Reinforcement Learning for Optimal Stopping with Application in Financial EngineeringCode0
An Empirical Study of Deep Reinforcement Learning in Continuing TasksCode0
Deep reinforcement learning for time series: playing idealized trading gamesCode0
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