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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Learning to learn with active adaptive perception.

D M Bossens1, N C Townsend1, A J Sobey1

  • 1Building 176 Boldrewood Innovation Campus, University of Southampton, Burgess Road, Southampton SO16 7QF, United Kingdom.

Neural Networks : the Official Journal of the International Neural Network Society
|April 9, 2019
PubMed
Summary
This summary is machine-generated.

Autonomous agents need general intelligence for long-term missions. This study introduces active adaptive perception, enabling agents to modify their perception for better learning in complex environments.

Keywords:
Adaptive perceptionInductive biasPartial observabilityReinforcement learningSelf-modifying policies

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Robotics

Background:

  • Autonomous agents require general intelligence for long-term missions due to sparse human feedback and the inability to pre-prescribe all behaviors.
  • Current deep reinforcement learning (DRL) methods have fixed updating routines, creating inductive biases that limit their ability to solve certain spatio-temporal pattern learning tasks.

Purpose of the Study:

  • Propose and evaluate an active adaptive perception (AAP) architecture for autonomous agents.
  • Enable agents to learn when and how to modify and selectively utilize their perception modules.
  • Address limitations of fixed training strategies in DRL and previous self-modifying policy (SMP) approaches.

Main Methods:

  • Introduced a generic architecture based on a self-modifying policy (SMP).
  • Implemented the SMP using Incremental Self-improvement with the Success Story Algorithm.
  • Compared two implementations (computationally cheap and expensive) against Deep Recurrent Q-Network (DRQN) and a basic SMP on non-episodic, partially observable mazes.

Main Results:

  • The proposed AAP architecture demonstrated emergent strategies to navigate complex environments, avoiding detrimental areas.
  • The computationally cheaper implementation showed effectiveness with a simple instruction set.
  • The more expensive implementation enabled selective ignoring of inaccurate perception data, improving performance.

Conclusions:

  • Active adaptive perception offers a viable solution for enhancing the general intelligence of autonomous agents in long-term missions.
  • The SMP architecture, particularly with adaptive perception, overcomes limitations of fixed DRL training strategies.
  • The study highlights the importance of flexible and adaptive perception mechanisms for robust autonomous operation.