Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning

Guy Davidson, Brenden M. Lake

Research output: Contribution to conferencePaperpeer-review

Abstract

We explore the benefits of augmenting state-of-the-art model-free deep reinforcement learning with simple object representations. Following the Frostbite challenge posited by Lake et al. (2017), we identify object representations as a critical cognitive capacity lacking from current reinforcement learning agents. We discover that providing the Rainbow model (Hessel et al., 2018) with simple, feature-engineered object representations substantially boosts its performance on the Frostbite game from Atari 2600. We then analyze the relative contributions of the representations of different types of objects, identify environment states where these representations are most impactful, and examine how these representations aid in generalizing to novel situations.

Original languageEnglish (US)
Pages2023-2029
Number of pages7
StatePublished - 2020
Event42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020 - Virtual, Online
Duration: Jul 29 2020Aug 1 2020

Conference

Conference42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020
CityVirtual, Online
Period7/29/208/1/20

Keywords

  • deep reinforcement learning
  • DQN
  • model-free reinforcement learning
  • object representations

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Human-Computer Interaction
  • Cognitive Neuroscience

Fingerprint

Dive into the research topics of 'Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning'. Together they form a unique fingerprint.

Cite this