  In the early stages of human life, babies develop their skills by exploring
different scenarios motivated by their inherent satisfaction rather than by
extrinsic rewards from the environment. This behavior, referred to as intrinsic
motivation, has emerged as one solution to address the exploration challenge
derived from reinforcement learning environments with sparse rewards. Diverse
exploration approaches have been proposed to accelerate the learning process
over single- and multi-agent problems with homogeneous agents. However, scarce
studies have elaborated on collaborative learning frameworks between
heterogeneous agents deployed into the same environment, but interacting with
different instances of the latter without any prior knowledge. Beyond the
heterogeneity, each agent's characteristics grant access only to a subset of
the full state space, which may hide different exploration strategies and
optimal solutions. In this work we combine ideas from intrinsic motivation and
transfer learning. Specifically, we focus on sharing parameters in actor-critic
model architectures and on combining information obtained through intrinsic
motivation with the aim of having a more efficient exploration and faster
learning. We test our strategies through experiments performed over a modified
ViZDooM's My Way Home scenario, which is more challenging than its original
version and allows evaluating the heterogeneity between agents. Our results
reveal different ways in which a collaborative framework with little additional
computational cost can outperform an independent learning process without
knowledge sharing. Additionally, we depict the need for modulating correctly
the importance between the extrinsic and intrinsic rewards to avoid undesired
agent behaviors.
