  In multi-agent navigation, agents need to move towards their goal locations
while avoiding collisions with other agents and static obstacles, often without
communication with each other. Existing methods compute motions that are
optimal locally but do not account for the aggregated motions of all agents,
producing inefficient global behavior especially when agents move in a crowded
space. In this work, we develop methods to allow agents to dynamically adapt
their behavior to their local conditions. We accomplish this by formulating the
multi-agent navigation problem as an action-selection problem, and propose an
approach, ALAN, that allows agents to compute time-efficient and collision-free
motions. ALAN is highly scalable because each agent makes its own decisions on
how to move using a set of velocities optimized for a variety of navigation
tasks. Experimental results show that the agents using ALAN, in general, reach
their destinations faster than using ORCA, a state-of-the-art collision
avoidance framework, the Social Forces model for pedestrian navigation, and a
Predictive collision avoidance model.
