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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 251–275 of 1918 papers

TitleStatusHype
Q-Adapter: Customizing Pre-trained LLMs to New Preferences with Forgetting MitigationCode1
Artificial Intelligence and Algorithmic Price Collusion in Two-sided Markets—0
Two-Step Q-Learning—0
Rethinking Data Augmentation for Robust LiDAR Semantic Segmentation in Adverse WeatherCode2
A Deep Reinforcement Learning Approach to Battery Management in Dairy Farming via Proximal Policy Optimization—0
Model-based Offline Reinforcement Learning with Lower Expectile Q-Learning—0
Towards Secure and Efficient Data Scheduling for Vehicular Social Networks—0
Contextualized Hybrid Ensemble Q-learning: Learning Fast with Control PriorsCode0
Decentralized Semantic Traffic Control in AVs Using RL and DQN for Dynamic Roadblocks—0
Boosting Soft Q-Learning by BoundingCode0
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
Learning to Select Goals in Automated Planning with Deep-Q Learning—0
Equivariant Offline Reinforcement 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
PlanDQ: Hierarchical Plan Orchestration via D-Conductor and Q-PerformerCode1
Fast-Fading Channel and Power Optimization of the Magnetic Inductive Cellular Network—0
Online Frequency Scheduling by Learning Parallel Actions—0
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