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arXiv · 1612.05159

Separation of Concerns in Reinforcement Learning

Harm van Seijen·Mehdi Fatemi·Joshua Romoff·Romain Laroche
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Abstract

In this paper, we propose a framework for solving a single-agent task by using multiple agents, each focusing on different aspects of the task. This approach has two main advantages: 1) it allows for training specialized agents on different parts of the task, and 2) it provides a new way to transfer knowledge, by transferring trained agents. Our framework generalizes the traditional hierarchical decomposition, in which, at any moment in time, a single agent has control until it has solved its particular subtask. We illustrate our framework with empirical experiments on two domains.

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Harm van Seijen, Mehdi Fatemi, Joshua Romoff, Romain Laroche. 2017-03-28. Separation of Concerns in Reinforcement Learning. https://arxiv.org/abs/1612.05159

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