  The Multi-agent Path Finding (MAPF) problem involves finding collision-free
paths for a team of agents in a known, static environment, with important
applications in warehouse automation, logistics, or last-mile delivery. To meet
the needs of these large-scale applications, current learning-based methods
often deploy the same fully trained, decentralized network to all agents to
improve scalability. However, such parameter sharing typically results in
homogeneous behaviors among agents, which may prevent agents from breaking ties
around symmetric conflict (e.g., bottlenecks) and might lead to
live-/deadlocks. In this paper, we propose SYLPH, a novel learning-based MAPF
framework aimed to mitigate the adverse effects of homogeneity by allowing
agents to learn and dynamically select different social behaviors (akin to
individual, dynamic roles), without affecting the scalability offered by
parameter sharing. Specifically, SYLPH agents learn to select their Social
Value Orientation (SVO) given the situation at hand, quantifying their own
level of selfishness/altruism, as well as an SVO-conditioned MAPF policy
dictating their movement actions. To these ends, each agent first determines
the most influential other agent in the system by predicting future
conflicts/interactions with other agents. Each agent selects its own SVO
towards that agent, and trains its decentralized MAPF policy to enact this SVO
until another agent becomes more influential. To further allow agents to
consider each others' social preferences, each agent gets access to the SVO
value of their neighbors. As a result of this hierarchical decision-making and
exchange of social preferences, SYLPH endows agents with the ability to reason
about the MAPF task through more latent spaces and nuanced contexts, leading to
varied responses that can help break ties around symmetric conflicts. [...]
