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arXiv · 1911.09535

Agent Probing Interaction Policies

Siddharth Ghiya·Oluwafemi Azeez·Brendan Miller
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Abstract

Reinforcement learning in a multi agent system is difficult because these systems are inherently non-stationary in nature. In such a case, identifying the type of the opposite agent is crucial and can help us address this non-stationary environment. We have investigated if we can employ some probing policies which help us better identify the type of the other agent in the environment. We've made a simplifying assumption that the other agent has a stationary policy that our probing policy is trying to approximate. Our work extends Environmental Probing Interaction Policy framework to handle multi agent environments.

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Siddharth Ghiya, Oluwafemi Azeez, Brendan Miller. 2019-11-21. Agent Probing Interaction Policies. https://arxiv.org/abs/1911.09535

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