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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 1851–1900 of 1918 papers

TitleStatusHype
Instance Weighted Incremental Evolution Strategies for Reinforcement Learning in Dynamic EnvironmentsCode0
Policy Iterations for Reinforcement Learning Problems in Continuous Time and Space -- Fundamental Theory and MethodsCode0
NARS vs. Reinforcement learning: ONA vs. Q-LearningCode0
Privacy-Preserving Q-Learning with Functional Noise in Continuous SpacesCode0
Privacy-preserving Q-Learning with Functional Noise in Continuous State SpacesCode0
A Multi-Step Minimax Q-learning Algorithm for Two-Player Zero-Sum Markov GamesCode0
Probing Implicit Bias in Semi-gradient Q-learning: Visualizing the Effective Loss Landscapes via the Fokker--Planck EquationCode0
Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy LearningCode0
A Machine with Short-Term, Episodic, and Semantic Memory SystemsCode0
Intelligent Masking: Deep Q-Learning for Context Encoding in Medical Image AnalysisCode0
Assumed Density Filtering Q-learningCode0
Propagating Uncertainty in Reinforcement Learning via Wasserstein BarycentersCode0
Robust Q-Learning for finite ambiguity setsCode0
Cooperation between Independent Market MakersCode0
Robust Q-Learning under Corrupted RewardsCode0
Solving Deep Reinforcement Learning Tasks with Evolution Strategies and Linear Policy NetworksCode0
Active exploration in parameterized reinforcement learningCode0
Solving NP-Hard Problems on Graphs with Extended AlphaGo ZeroCode0
Control with adaptive Q-learningCode0
The Mean-Squared Error of Double Q-LearningCode0
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
A Deep Recurrent Q Network towards Self-adapting Distributed Microservices architectureCode0
ISL: A novel approach for deep explorationCode0
Reinforcement-Learning based routing for packet-optical networks with hybrid telemetryCode0
Deep Reinforcement Learning for Imbalanced ClassificationCode0
Think Smart, Act SMARL! Analyzing Probabilistic Logic Shields for Multi-Agent Reinforcement LearningCode0
Provably efficient RL with Rich Observations via Latent State DecodingCode0
Nonparametric Stochastic Compositional Gradient Descent for Q-Learning in Continuous Markov Decision ProblemsCode0
Join Query Optimization with Deep Reinforcement Learning AlgorithmsCode0
Visual Exploration and Energy-aware Path Planning via Reinforcement LearningCode0
Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN TargetCode0
AlignIQL: Policy Alignment in Implicit Q-Learning through Constrained OptimizationCode0
Joint Path planning and Power Allocation of a Cellular-Connected UAV using Apprenticeship Learning via Deep Inverse Reinforcement LearningCode0
ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement LearningCode0
Classification with Costly Features using Deep Reinforcement LearningCode0
Active Collection of Well-Being and Health Data in Mobile DevicesCode0
QBSO-FS: A Reinforcement Learning Based Bee Swarm Optimization Metaheuristic for Feature SelectionCode0
Taming the Noise in Reinforcement Learning via Soft UpdatesCode0
Topological Experience ReplayCode0
A Kernel Loss for Solving the Bellman EquationCode0
Offline Reinforcement Learning for Learning to Dispatch for Job Shop SchedulingCode0
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