Generation of Traffic Flows in Multi-Agent Traffic Simulation with Agent Behavior Model based on Deep Reinforcement Learning
Generation of Traffic Flows in Multi-Agent Traffic Simulation with Agent Behavior Model based on Deep Reinforcement Learning
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发表时间：
2020-12
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通讯作者：
Junjie Zhong;Hiromitsu Hattori
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作者：
Junjie Zhong;Hiromitsu Hattori
在基于多智能体的交通仿真中，智能体总是按照已有的指令运动，机械地、不自然地模仿人类的行为。人类驾驶员总是不规则地执行加速或减速，这在某些情况下似乎是不必要的。为了让交通仿真中的智能体更像人类，并在复杂条件下识别其他智能体的行为，我们提出了一种统一的机制，通过使用深度强化学习，智能体学习决定各种加速度，基于重新生成的视觉图像显示一些显着的特征，和包含一些重要数据的数值向量，如瞬时速度。通过处理批量的顺序数据，使代理能够识别周围代理的行为，并决定自己的加速度。此外，我们可以生成一个交通流行为的duality来模拟真实的交通流，通过使用一个架构，完全分散的训练和完全集中的执行，而不违反马尔可夫假设。
In multi-agent based traffic simulation, agents are always supposed to move following existing instructions, and mechanically and unnaturally imitate human behavior. The human drivers perform acceleration or deceleration irregularly all the time, which seems unnecessary in some conditions. For letting agents in traffic simulation behave more like humans and recognize other agents' behavior in complex conditions, we propose a unified mechanism for agents learn to decide various accelerations by using deep reinforcement learning based on a combination of regenerated visual images revealing some notable features, and numerical vectors containing some important data such as instantaneous speed. By handling batches of sequential data, agents are enabled to recognize surrounding agents' behavior and decide their own acceleration. In addition, we can generate a traffic flow behaving diversely to simulate the real traffic flow by using an architecture of fully decentralized training and fully centralized execution without violating Markov assumptions.