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This study introduces macro-actions, or options, for decentralized partially observable Markov decision processes (Dec-POMDPs) to handle complex, long-term agent coordination under uncertainty. The new algorithms enable efficient policy generation for larger problems and longer horizons.

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Area of Science:

  • Artificial Intelligence
  • Robotics
  • Decision Theory

Background:

  • Decentralized partially observable Markov decision processes (Dec-POMDPs) are standard for multi-agent decision-making under uncertainty.
  • Traditional Dec-POMDPs use primitive, single-time-step actions, limiting scalability and temporal abstraction.
  • Handling temporally extended actions (macro-actions) in decentralized systems presents significant challenges due to asynchronous termination.

Purpose of the Study:

  • To extend existing Dec-POMDP algorithms to effectively model and solve problems involving macro-actions (options).
  • To enable decentralized agents to coordinate using temporally extended actions, improving scalability and solution quality.
  • To demonstrate the efficacy of the proposed approach in complex benchmarks and multi-robot coordination tasks.

Main Methods:

  • Modeling macro-actions as options within the Dec-POMDP framework, focusing on agent-specific information during execution.
  • Extending three leading Dec-POMDP policy generation algorithms to accommodate the asynchronous nature of options.
  • Evaluating the extended algorithms on standard benchmarks and a multi-robot coordination problem.

Main Results:

  • The developed algorithms successfully retain agent coordination capabilities with macro-actions.
  • High-quality solutions were generated for significantly longer horizons and larger state-spaces compared to prior Dec-POMDP methods.
  • The approach demonstrated effectiveness in multi-robot coordination, balancing uncertainty and sensor information.

Conclusions:

  • Macro-actions (options) can be effectively integrated into Dec-POMDPs to enhance decision-making capabilities for decentralized agents.
  • The extended algorithms offer a more scalable and versatile approach for complex multi-agent coordination problems.
  • This work provides a unified framework for synthesizing control policies that leverage coordination opportunities in uncertain environments.