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Multi-robot inverse reinforcement learning under occlusion with estimation of state transitions

  • Published:2018-10 Issue: Volume:263 Page:46-73
  • ISSN:0004-3702
  • Container-title:Artificial Intelligence
  • language:en
  • Short-container-title:Artificial Intelligence

Author:

Bogert KennethORCID,Doshi Prashant

Funder

NSF

ONR

Toyota Research Institute of North America

Publisher

Elsevier BV

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Reference37 articles.

1. Apprenticeship learning via inverse reinforcement learning;Abbeel,2004

2. Multi-robot perimeter patrol in adversarial settings;Agmon,2008

3. A survey of robot learning from demonstration;Argall;Robot. Auton. Syst.,2009

4. The complexity of multi-agent plan recognition;Banerjee;Auton. Agents Multi-Agent Syst.,2015

5. Estimation of state probabilities using the maximum entropy principle;Bard;IBM J. Res. Dev.,1980

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1. Memory-Augmented Lyapunov-Based Safe Reinforcement Learning: End-to-End Safety Under Uncertainty;IEEE Transactions on Artificial Intelligence;2023-12

2. LEMURS: Learning Distributed Multi-Robot Interactions;2023 IEEE International Conference on Robotics and Automation (ICRA);2023-05-29

3. Inverse Reinforcement Learning as the Algorithmic Basis for Theory of Mind: Current Methods and Open Problems;Algorithms;2023-01-19

4. Robot learning towards smart robotic manufacturing: A review;Robotics and Computer-Integrated Manufacturing;2022-10

5. Study on Deep Reinforcement Learning for Mobile Robots Flocking Control in Certainty Situations;2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON);2022-05-24
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