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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 351–360 of 1918 papers

TitleStatusHype
Dynamic operator management in meta-heuristics using reinforcement learning: an application to permutation flowshop scheduling problems—0
Optimizing TD3 for 7-DOF Robotic Arm Grasping: Overcoming Suboptimality with Exploration-Enhanced Contrastive Learning—0
Can LLM be a Good Path Planner based on Prompt Engineering? Mitigating the Hallucination for Path Planning—0
Deviations from the Nash equilibrium and emergence of tacit collusion in a two-player optimal execution game with reinforcement learning—0
GINO-Q: Learning an Asymptotically Optimal Index Policy for Restless Multi-armed Bandits—0
Improved Q-learning based Multi-hop Routing for UAV-Assisted Communication—0
A Conflicts-free, Speed-lossless KAN-based Reinforcement Learning Decision System for Interactive Driving in Roundabouts—0
Variance-Reduced Cascade Q-learning: Algorithms and Sample Complexity—0
A Geometric Nash Approach in Tuning the Learning Rate in Q-Learning Algorithm—0
Crowd Intelligence for Early Misinformation Prediction on Social MediaCode0
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