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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 401–425 of 1918 papers

TitleStatusHype
Decentralized Semantic Traffic Control in AVs Using RL and DQN for Dynamic Roadblocks—0
MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Intervention—0
A General Control-Theoretic Approach for Reinforcement Learning: Theory and Algorithms—0
Equivariant Offline Reinforcement Learning—0
Learning to Select Goals in Automated Planning with Deep-Q Learning—0
EduQate: Generating Adaptive Curricula through RMABs in Education Settings—0
Reinforcement-Learning based routing for packet-optical networks with hybrid telemetryCode0
Catalytic evolution of cooperation in a population with behavioural bimodality—0
Optimal Transport-Assisted Risk-Sensitive Q-Learning—0
Mix Q-learning for Lane Changing: A Collaborative Decision-Making Method in Multi-Agent Deep Reinforcement Learning—0
Finite-Time Analysis of Simultaneous Double Q-learning—0
Probing Implicit Bias in Semi-gradient Q-learning: Visualizing the Effective Loss Landscapes via the Fokker--Planck EquationCode0
Multi-agent Reinforcement Learning with Deep Networks for Diverse Q-Vectors—0
Online Frequency Scheduling by Learning Parallel Actions—0
Stabilizing Extreme Q-learning by Maclaurin ExpansionCode0
Fast-Fading Channel and Power Optimization of the Magnetic Inductive Cellular Network—0
Bootstrapping Expectiles in Reinforcement Learning—0
Age of Trust (AoT): A Continuous Verification Framework for Wireless Networks—0
Algorithmic Collusion in Dynamic Pricing with Deep Reinforcement Learning—0
Tabular and Deep Learning for the Whittle Index—0
How to discretize continuous state-action spaces in Q-learning: A symbolic control approach—0
Target Networks and Over-parameterization Stabilize Off-policy Bootstrapping with Function ApproximationCode0
Q-learning as a monotone scheme—0
Approximate Global Convergence of Independent Learning in Multi-Agent Systems—0
Federated Q-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost—0
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