Anonymous authors
Paper under double-blind review
ABSTRACT
Autonomous vehicles are becoming more common in city transportation. Companies
will begin to find a need to teach these vehicles smart city fleet coordination.
Currently, simulation based modeling along with hand coded rules dictate
the decision making of these autonomous vehicles. We believe that complex intelligent
behavior can be learned by these agents through Reinforcement Learning.
In this paper, we discuss our work for solving this system by adapting the Deep
Q-Learning (DQN) model to the multi-agent setting. Our approach applies deep
reinforcement learning by combining convolutional neural networks with DQN to
teach agents to fulfill customer demand in an environment that is partially observable
to them. We also demonstrate how to utilize transfer learning to teach agents
to balance multiple objectives such as navigating to a charging station when its energy
level is low. The two evaluations presented show that our solution has shown
that we are successfully able to teach agents cooperation policies while balancing
multiple objectives.
AUTONOMOUS VEHICLE FLEET COORDINATION WITH DEEP REINFORCEMENT LEARNING
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