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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 1576–1600 of 1918 papers

TitleStatusHype
Risk-Sensitive Compact Decision Trees for Autonomous Execution in Presence of Simulated Market Response—0
Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning—0
Escaping the State of Nature: A Hobbesian Approach to Cooperation in Multi-agent Reinforcement Learning—0
Reinforcement Learning with Low-Complexity Liquid State MachinesCode0
On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collection—0
Stabilizing Off-Policy Q-Learning via Bootstrapping Error ReductionCode0
Feature-Based Q-Learning for Two-Player Stochastic Games—0
RSS-Based Q-Learning for Indoor UAV Navigation—0
Provably Efficient Q-Learning with Low Switching Cost—0
Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodology—0
Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning—0
Solving NP-Hard Problems on Graphs with Extended AlphaGo ZeroCode0
A General Markov Decision Process Framework for Directly Learning Optimal Control Policies—0
Finite-Sample Analysis of Nonlinear Stochastic Approximation with Applications in Reinforcement LearningCode0
Prioritized Sequence Experience Replay—0
A Kernel Loss for Solving the Bellman EquationCode0
Adaptive Symmetric Reward Noising for Reinforcement LearningCode0
Neural Temporal-Difference and Q-Learning Provably Converge to Global OptimaCode0
MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning—0
Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment—0
Deep Reinforcement Learning Based Parameter Control in Differential EvolutionCode0
Stochastic Variance Reduction for Deep Q-learning—0
Reinforcement Learning for Learning of Dynamical Systems in Uncertain Environment: a Tutorial—0
QBSO-FS: A Reinforcement Learning Based Bee Swarm Optimization Metaheuristic for Feature SelectionCode0
Autonomous Penetration Testing using Reinforcement Learning—0
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