Social Learning In Markov Games: Empowering Autonomous Driving
Social Learning In Markov Games: Empowering Autonomous Driving
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马尔可夫博弈中的社交学习：赋能自动驾驶
DOI：
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Xuan Di
中科院分区：
文献类型：
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作者：
Xu Chen;Zechu Li;Xuan Di
在多智能体系统（MAS）中，社会学习方案允许独立的智能体通过与从池中随机选择的智能体的交互来学习。这样的方案对于自主车辆（AV）在由许多道路使用者组成的复杂交通环境中导航是重要的。在本文中，我们将社会学习方案应用于马尔可夫博弈，并利用深度强化学习（DRL）来研究单个AV如何学习策略并在交通场景中形成社会规范。为了捕捉代理对交通环境的不同态度，一个异构的代理池与合作和有缺陷的AV的社会学习计划。为了解决无人驾驶汽车形成的社会规范，我们提出了一种DRL算法，并将其应用于交通场景：无信号交叉口和高速公路排队。我们发现，相比有缺陷的AV，合作AV可以很容易地符合预期的社会规范。此外，合作的自动驾驶汽车将导致更低的撞车率。我们还发现，优先道路/车道可以使自动驾驶汽车符合预期的社会规范。
In a multi-agent system (MAS), a social learning scheme allows independent agents to learn through interactions with agents randomly selected from a pool. Such a scheme is important for autonomous vehicles (AV) to navigate complex traffic environments consisting of many road users. In this paper, we apply the social learning scheme to Markov games and leverage deep reinforcement learning (DRL) to investigate how individual AVs learn policies and form social norms in traffic scenarios. To capture agents’ different attitudes toward traffic environments, a heterogeneous agent pool with cooperative and defective AVs is introduced to the social learning scheme. To solve social norms formed by AVs, we propose a DRL algorithm, and apply them to traffic scenarios: unsignalized intersection and highway platoon. We find that compared to defective AVs, cooperative AVs can easily conform to expected social norms. In addition, cooperative AVs would lead to lower crash rates. We also find that prioritized roads/lanes can make AVs conform to expected social norms.