TRAINING INTELLIGENT TENNIS ADVERSARIES USING SELF-PLAY WITH ML AGENTS
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Date
2021-05
Authors
Ospanov, Bakhtiyar
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
Abstract
In the game and robotics industries, the design of intelligent and interactive characters
can be greatly enriched by advances in artificial intelligence. This approach is in
need due to the biased and pre-programmed, and thus limited, nature of conventional
algorithms. In contrast, machine learning approaches can educate these characters to
have creative and independent behavior even in complex games. This study explores
the possibility of training intelligent adversaries using self-play in a game of tennis,
which is not yet competently researched. The agent is provided with the basic tennis
rules and informed about the outcome (winning/losing). Given that, it is up to the
agent to find out suitable behavior. The agents are placed into a visually, physically,
and cognitively rich surrounding environment implemented in Unity. Reinforcement
learning with proximal policy optimization is used to train one brain for two adversarial
agents. The training is stabilized using incremental complexity modification
based on curriculum learning. By having itself as a level matching opponent, the
agent consistently improves its skills. The Elo rating system helps to quantitatively
assess the performance by computing the relative skill level between two agents in a
zero-sum game. The liberty in behavior during the training process has opened the
possibility for agents to discover robust tactics that helped them to play on the same
level as human opponents. Later, an agent’s brain can be employed for researching,
benchmarking, and using as virtual non-playable characters.
Description
Keywords
tennis, self-play, Research Subject Categories::TECHNOLOGY, Type of access: Open Access
Citation
"Ospanov, B. (2021). Training Intelligent Tennis Adversaries Using Self-Play With ML Agents (Unpublished master's thesis). Nazarbayev University, Nur-Sultan, Kazakhstan"