We present the single track road problem. In this problem two agents face each-other at opposite positions of a road that can only have one agent pass at a time. We focus on the scenario in which one agent is human, while the other is an autonomous agent. We run experiments with human subjects in a simple grid domain, which simulates the single track road problem. We show that when data is limited, building an accurate human model is very challenging, and that a reinforcement learning agent, which is based on this data, does not perform well in practice. However, we show that an agent that tries to maximize a linear combination of the human's utility and its own utility, achieves a high score, and significantly outperforms other baselines, including an agent that tries to maximize only its own utility.
翻译:我们展示了单轨道路问题。 在这个问题中,两个代理物在一条只能同时有一个代理物通过的道路的对立位置上相互对峙。 我们集中关注一个代理物是人类的情景, 而另一个则是自主代理物。 我们在一个简单的网格域内对人体实验, 模拟单一轨道道路问题。 我们显示,当数据有限时, 构建一个准确的人类模型是非常困难的, 而基于这些数据的强化学习代理物在实践上效果不佳。 然而, 我们显示, 一个试图最大限度地将人类的效用和其自身的效用进行线性组合的代理物取得了很高的分数, 并且大大超过其他基线, 包括一个试图仅尽量扩大自身效用的代理物。