  Being able to navigate to a target with minimal supervision and prior
knowledge is critical to creating human-like assistive agents. Prior work on
map-based and map-less approaches have limited generalizability. In this paper,
we present a novel approach, Hybrid Asynchronous Universal Successor
Representations (HAUSR), which overcomes the problem of generalizability to new
goals by adapting recent work on Universal Successor Representations with
Asynchronous Actor-Critic Agents. We show that the agent was able to
successfully reach novel goals and we were able to quickly fine-tune the
network for adapting to new scenes. This opens up novel application scenarios
where intelligent agents could learn from and adapt to a wide range of
environments with minimal human input.
