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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 1151–1175 of 1918 papers

TitleStatusHype
SOLO: Search Online, Learn Offline for Combinatorial Optimization Problems—0
Federated Double Deep Q-learning for Joint Delay and Energy Minimization in IoT networks—0
Regularized Softmax Deep Multi-Agent Q-Learning—0
Reinforcement Learning based on Scenario-tree MPC for ASVs—0
Variational quantum compiling with double Q-learning—0
Convergence of Finite Memory Q-Learning for POMDPs and Near Optimality of Learned Policies under Filter Stability—0
S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning—0
A Jointly Optimal Design of Control and Scheduling in Networked Systems under Denial-of-Service Attacks—0
The Effect of Q-function Reuse on the Total Regret of Tabular, Model-Free, Reinforcement Learning—0
Decentralized Microgrid Energy Management: A Multi-agent Correlated Q-learning Approach—0
Correlated Deep Q-learning based Microgrid Energy Management—0
UCB Momentum Q-learning: Correcting the bias without forgettingCode0
Ensemble Bootstrapping for Q-Learning—0
Potential Impacts of Smart Homes on Human Behavior: A Reinforcement Learning Approach—0
Reinforcement learning approach for resource allocation in humanitarian logistics—0
No-Regret Reinforcement Learning with Heavy-Tailed Rewards—0
Balancing Rational and Other-Regarding Preferences in Cooperative-Competitive EnvironmentsCode0
Sequential Learning-based IaaS Composition—0
Greedy-Step Off-Policy Reinforcement Learning—0
Understanding algorithmic collusion with experience replayCode0
A Discrete-Time Switching System Analysis of Q-learning—0
Cooperation and Reputation Dynamics with Reinforcement Learning—0
Reversible Action Design for Combinatorial Optimization with Reinforcement Learning—0
Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis—0
Hedging of Financial Derivative Contracts via Monte Carlo Tree Search—0
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