Online heterogeneous multiagent learning under limited communication with applications to forest fire management

Keyphrases

• Convergence Rate 100%
• Multi-agent Learning 100%
• Heterogeneous Agents 100%
• Limited Communication 100%
• Forest Fire Management 100%
• Learning Problems 66%
• Collaborative Filtering 66%
• Incremental Feature 66%
• Feature Dependency 66%
• Dependency Discovery 66%
• Number of Features 33%
• Model Complexity 33%
• Closed-loop 33%
• Processing Unit 33%
• Information Sharing 33%
• State Transition Model 33%
• Transition Model 33%
• Learning Algorithm 33%
• Multi-agent 33%
• Online Estimation 33%
• Model Transfer 33%
• Fire Spread 33%
• Management Scenarios 33%
• Joint Learning 33%
• Communication Cost 33%
• Heterogeneous Scenario 33%
• Actual Model 33%
• Improved Convergence 33%
• Linear Function Approximation 33%
• Robotic Missions 33%
• Modeling Error Reduction 33%
• Central Processing 33%

Computer Science

• Convergence Rate 100%
• multiagent learning 100%
• Transition Model 66%
• Learning Problem 66%
• Collaborative Filtering 66%
• State Transition 33%
• Function Approximation 33%
• Model Complexity 33%
• multi-agent 33%
• Flight Hardware 33%
• Communication Cost 33%
• Centralized Processing 33%
• Processing Unit 33%
• Learning Algorithm 33%
• Linear Function 33%
• Share Information 33%