  Recently, autonomous driving has made substantial progress in addressing the
most common traffic scenarios like intersection navigation and lane changing.
However, most of these successes have been limited to scenarios with
well-defined traffic rules and require minimal negotiation with other vehicles.
In this paper, we introduce a previously unconsidered, yet everyday,
high-conflict driving scenario requiring negotiations between agents of equal
rights and priorities. There exists no centralized control structure and we do
not allow communications. Therefore, it is unknown if other drivers are willing
to cooperate, and if so to what extent. We train policies to robustly negotiate
with opposing vehicles of an unobservable degree of cooperativeness using
multi-agent reinforcement learning (MARL). We propose Discrete Asymmetric Soft
Actor-Critic (DASAC), a maximum-entropy off-policy MARL algorithm allowing for
centralized training with decentralized execution. We show that using DASAC we
are able to successfully negotiate and traverse the scenario considered over
99% of the time. Our agents are robust to an unknown timing of opponent
decisions, an unobservable degree of cooperativeness of the opposing vehicle,
and previously unencountered policies. Furthermore, they learn to exhibit
human-like behaviors such as defensive driving, anticipating solution options
and interpreting the behavior of other agents.

Machine Learning;Artificial Intelligence;Multiagent Systems;Robotics