# VUSFA:Variational Universal Successor Features Approximator to Improve Transfer DRL for Target Driven Visual Navigation

arXiv: https://arxiv.org/abs/1908.06376 · HTML: https://mlsanity.com/paper/1908.06376/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [Maximum State Entropy Exploration using Predecessor and Successor Representations](https://mlsanity.com/paper/2306.14808/) (2023)
- [Understanding the Complexity Gains of Single-Task RL with a Curriculum](https://mlsanity.com/paper/2212.12809/) (2022)
- [A New Representation of Successor Features for Transfer across Dissimilar Environments](https://mlsanity.com/paper/2107.08426/) (2021)
- [Uncertainty-aware transfer across tasks using hybrid model-based successor feature reinforcement learning](https://mlsanity.com/paper/2310.10818/) (2023)

## Abstract (arXiv)

In this paper, we show how novel transfer reinforcement learning techniques can be applied to the complex task of target driven navigation using the photorealistic AI2THOR simulator. Specifically, we build on the concept of Universal Successor Features with an A3C agent. We introduce the novel architectural contribution of a Successor Feature Dependant Policy (SFDP) and adopt the concept of Variational Information Bottlenecks to achieve state of the art performance. VUSFA, our final architecture, is a straightforward approach that can be implemented using our open source repository. Our approach is generalizable, showed greater stability in training, and outperformed recent approaches in terms of transfer learning ability.