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Autonomous learning of features for control: Experiments with embodied and situated agents.

Nicola Milano¹, Stefano Nolfi¹

¹Institute of Cognitive Science and Technologies, National Research Council (CNR-ISTC), Roma, Italy.

|April 15, 2021

Summary

We introduce a method for continuous control optimization using self-supervised feature extraction. Parallel training of feature and control networks enhances agent performance, especially with egocentric observations.

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Area of Science:

Artificial Intelligence
Machine Learning
Robotics

Background:

Continuous control optimization is crucial for intelligent agents.
Enhancing reinforcement learning (RL) and evolutionary algorithms (EA) is an active research area.
Self-supervised learning offers a promising avenue for feature extraction.

Purpose of the Study:

To introduce a novel method for continuous control optimization.
To enable parallel training of feature extraction and control networks.
To investigate the benefits of self-supervised feature extraction in continuous control tasks.

Main Methods:

Developed a method for concurrent training of feature extraction and control networks.
Employed self-supervision for training the feature extraction network.
Evaluated the approach on continuous control problems, including those with egocentric observations.
Compared sequence-to-sequence learning against alternative feature extraction methods.

Main Results:

Parallel training of feature and control networks is critical for agents using egocentric observations.
Feature extraction provides benefits even in problems where dimensionality reduction is not the primary goal.
Sequence-to-sequence learning demonstrated superior performance compared to other considered feature extraction techniques.

Conclusions:

The proposed method enhances the efficacy of evolutionary and reinforcement learning algorithms.
Self-supervised feature extraction, particularly with sequence-to-sequence models, offers significant advantages in continuous control.
The parallel training strategy is vital for agents relying on egocentric viewpoints.