  Inferring other agents' mental states such as their knowledge, beliefs and
intentions is thought to be essential for effective interactions with other
agents. Recently, multiagent systems trained via deep reinforcement learning
have been shown to succeed in solving different tasks, but it remains unclear
how each agent modeled or represented other agents in their environment. In
this work we test whether deep reinforcement learning agents explicitly
represent other agents' intentions (their specific aims or goals) during a task
in which the agents had to coordinate the covering of different spots in a 2D
environment. In particular, we tracked over time the performance of a linear
decoder trained to predict the final goal of all agents from the hidden state
of each agent's neural network controller. We observed that the hidden layers
of agents represented explicit information about other agents' goals, i.e. the
target landmark they ended up covering. We also performed a series of
experiments, in which some agents were replaced by others with fixed goals, to
test the level of generalization of the trained agents. We noticed that during
the training phase the agents developed a differential preference for each
goal, which hindered generalization. To alleviate the above problem, we propose
simple changes to the MADDPG training algorithm which leads to better
generalization against unseen agents. We believe that training protocols
promoting more active intention reading mechanisms, e.g. by preventing simple
symmetry-breaking solutions, is a promising direction towards achieving a more
robust generalization in different cooperative and competitive tasks.
