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partially-observable-markov-decision-process

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Learnable MAPF. “Distributed Heuristic Multi-Agent Path Finding with Communication” (DHC) algorithm from ICRA 2021 is implemented and benchmarked in out-of-distribution (OOD) scenarios. A new robust training loop to handle communication failures is introduced.

  • Updated Nov 9, 2023
  • Python

This repository contains Dongming Shen's code and documentation for the research projects conducted at the AIDyS Lab, USC. The project focuses on integrating Reinforcement Learning (RL) to solve partially observable Markov decision processes (POMDP) under finite linear temporal logic (LTL) constraints.

  • Updated May 24, 2024
  • C++

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