  Training multiple agents to perform safe and cooperative control in the
complex scenarios of autonomous driving has been a challenge. For a small fleet
of cars moving together, this paper proposes Lepus, a new approach to training
multiple agents. Lepus adopts a pure cooperative manner for training multiple
agents, featured with the shared parameters of policy networks and the shared
reward function of multiple agents. In particular, Lepus pre-trains the policy
networks via an adversarial process, improving its collaborative
decision-making capability and further the stability of car driving. Moreover,
for alleviating the problem of sparse rewards, Lepus learns an approximate
reward function from expert trajectories by combining a random network and a
distillation network. We conduct extensive experiments on the MADRaS simulation
platform. The experimental results show that multiple agents trained by Lepus
can avoid collisions as many as possible while driving simultaneously and
outperform the other four methods, that is, DDPG-FDE, PSDDPG, MADDPG, and
MAGAIL(DDPG) in terms of stability.
