Research in trajectory forecasting (i.e., predicting where an agent will be in the future) has grown

Cooperative multi-agent reinforcement learning (MARL) has been proposed for multi-agent collaborations toaccomplish manychallenging tasks[1,2,3,4].

Cooperative multi-agent reinforcement learning (MARL) has been proposed for multi-agent collaborations toaccomplish manychallenging tasks[1,2,3,4].

We present a scalable approach for learningopen-world object-goal navigation (ObjectNav) - the task of asking a virtual robot (agent) to find any instance of an object in an unexplored environment (e.g.,"find a sink").

Modelling the dynamics of multi-agent learning has long been an important research topic, but all of the previous works focus on2-agent settings and mostly use evolutionary game theoretic approaches.