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.
Cite
@article{arxiv.1811.11312,
title = {Target Driven Visual Navigation with Hybrid Asynchronous Universal Successor Representations},
author = {Shamane Siriwardhana and Rivindu Weerasekera and Suranga Nanayakkara},
journal= {arXiv preprint arXiv:1811.11312},
year = {2018}
}
Comments
Deep Reinforcement Learning Workshop, NeurIPS 2018