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Related Experiment Video

Updated: Jan 4, 2026

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

Reinforcement Learning for Improving Agent Design.

¹Google Brain, Tokyo, Japan. hadavid@google.com.

Artificial Life

|November 8, 2019

Summary

This summary is machine-generated.

This study introduces a novel approach where agents learn to optimize their physical design alongside their control policy. This joint learning enhances task performance and facilitates policy acquisition.

Keywords:

Neuroevolution deep reinforcement learning evolution strategies generative design

Related Experiment Videos

Last Updated: Jan 4, 2026

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Investigating Motor Skill Learning Processes with a Robotic Manipulandum

Published on: February 12, 2017

Area of Science:

Artificial Intelligence
Robotics
Machine Learning

Background:

Traditional reinforcement learning focuses on optimizing agent policies with fixed physical designs.
Agent morphology is rarely adapted for specific tasks, limiting performance.

Purpose of the Study:

To investigate the joint learning of an agent's physical structure and its control policy.
To explore if optimizing agent design can improve task performance and learning efficiency.

Main Methods:

Modified the OpenAI Gym framework to parameterize environment components.
Enabled agents to learn and modify these environment parameters concurrently with their policy.

Main Results:

Agents successfully learned improved physical structures tailored to their tasks.
The optimized structures facilitated more efficient policy learning.
Joint policy and structure learning uncovered potential design principles.

Conclusions:

Learning agent design and policy simultaneously leads to better task-specific adaptations.
This approach can enhance reinforcement learning performance and inform assisted-design processes.