  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.
