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Autonomous learning based on cost assumptions: theoretical studies and experiments in robot control.

C H Ribeiro¹, E M Hemerly

¹Technological Institute of Aeronautics, São José Dos Campos - SP, Brazil.

International Journal of Neural Systems

|May 8, 2000

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This study introduces a generalized Q-learning method for autonomous learning systems. It enables efficient experience acquisition by spreading information, improving reinforcement learning in robot navigation.

Area of Science:

Artificial Intelligence
Machine Learning
Robotics

Background:

Autonomous learning relies on experience acquisition, which is often time-consuming in realistic applications.
Classical learning algorithms struggle with the crudeness of information from sensor readings, actuator control, and algorithmic updates.
There's a need to embed facilitating information into learning processes without compromising fundamental algorithm structures.

Purpose of the Study:

To investigate a generalized formulation of the Q-learning method.
To enable the spreading of information from single updates to neighboring states, facilitating faster convergence.
To demonstrate a mechanism for safely embedding prior knowledge into the state space structure.

Main Methods:

Developed a generalized Q-learning formulation that allows information propagation to neighboring states.

Related Experiment Videos

Modified a reinforcement learning algorithm to incorporate this generalized Q-learning approach.

Tested the modified algorithm in a real-world robot navigation task.

Main Results:

The generalized Q-learning method converges to optimality by spreading information.
The formulation effectively embeds prior knowledge about the state space structure.
Demonstrated successful application in a practical robot navigation scenario.

Conclusions:

The proposed generalized Q-learning method enhances autonomous learning efficiency.
This approach offers a robust way to integrate prior knowledge into reinforcement learning.
The findings are validated through a real robot navigation task, showing practical applicability.