Summary of N-agent Ad Hoc Teamwork, by Caroline Wang et al.
N-Agent Ad Hoc Teamwork
by Caroline Wang, Arrasy Rahman, Ishan Durugkar, Elad Liebman, Peter Stone
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces N-agent ad hoc teamwork (NAHT), a framework for learning cooperative behaviors in scenarios where autonomous agents interact with dynamically varying numbers and types of teammates. Current approaches assume restrictive settings or control over all agents, but NAHT addresses this gap by formalizing the problem and proposing the Policy Optimization with Agent Modelling (POAM) algorithm. POAM is a policy gradient, multi-agent reinforcement learning approach that learns representations of teammate behaviors to adapt to diverse teammate interactions. The paper evaluates POAM on tasks from the multi-agent particle environment and StarCraft II, showing improved cooperative task returns compared to baseline approaches and enabling out-of-distribution generalization to unseen teammates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where cars can work together seamlessly, like ants or bees. But what if these cars have to work with cars from other companies? That’s the problem this paper tries to solve. They introduce a new way for machines to learn how to cooperate with each other in situations that are changing all the time. The method is called Policy Optimization with Agent Modelling (POAM). It helps machines understand what other machines can do and adapt to work together better. The researchers tested POAM on some games and found it did much better than other methods at making good decisions. This could be really important for self-driving cars, robots, or even military teams working together. |
Keywords
» Artificial intelligence » Generalization » Optimization » Reinforcement learning
