Abstract:

Learning how to adjust to an opponent's position is critical to the success of having intelligent agents collaborating towards the achievement of specific tasks in unfriendly environments. This paper describes our work on developing methods to learn to choose an action based on a continuous-valued state attribute indicating the position of an opponent. We use a framework in which teams of agents compete in a simulator of a game of robotic soccer. We introduce a memory-based supervised learning strategy which enables an agent to choose to pass or shoot in the presence of a defender. In our memory model, training examples affect neighboring generalized learned instances with different weights. We conduct experiments in which the agent incrementally learns to approximate a function with a continuous domain. Then we investigate the question of how the agent performs in nondeterministic variations of the training situations. Our experiments indicate that when the random variations fall within some bound of the initial training, the agent performs better with some initial training rather than from a tabula-rasa.

Keywords:
Robotic Soccer, Memory-Based Learning, Incremental Learning, Adaptive Learning, Continuous-function Learning

Tech Report Number:
CMU-CS-95-222

Beating a Defender in Robotic Soccer: Memory-Based Learning of a Continuous Function

Peter Stone and Manuela Veloso

• Introduction
• Learning Method
  • Motivation
  • Experimental Setup
  • Memory Model
    • Storing to Memory
    • Retrieving from Memory
  • Choosing an Action
• Experiments and Results
  • Memory Size
  • Adaptive Memory
  • Coping with Noise
• Related and Future Works
• Conclusion
• References
• About this document ...