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- Abstract: In this paper, we show how novel transfer reinforcement learning techniques can be applied to the complex task of target-driven navigation using the photorealisticAI2THOR simulator. Specifically, we build on the concept of Universal SuccessorFeatures with an A3C agent. We introduce the novel architectural1contribution of a Successor Feature Dependent Policy (SFDP) and adopt the concept of VariationalInformation 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.
- Keywords: Universal Successor Features, Successor Features, Model Free Deep Reinforcement Learning
- TL;DR: We present an improved version of Universal Successor Features based DRL method which can improve the transfer learning of agents.