wargus
Jaidee
We present CLASSQ-L (for: class Q-learning) an application of the Q-learning reinforcement learning algorithm to play complete Wargus games. Wargus is a real-time strategy game where players control armies consisting of units of different classes (e.g., archers, knights). CLASSQ-L uses a single table for each class of unit so that each unit is controlled and updates its class' Q-table. This enables rapid learning as in Wargus there are many units of the same class.
CLASSQ-L: A Q-Learning Algorithm for Adversarial Real-Time Strategy Games
Jaidee, Ulit (Lehigh University) | Munoz-Avila, Hector (Lehigh University)
We present CLASS Q-L (for: class Q-learning) an application of the Q-learning reinforcement learning algorithm to play complete Wargus games. Wargus is a real-time strategy game where players control armies consisting of units of different classes (e.g., archers, knights). CLASS Q-L uses a single table for each class of unit so that each unit is controlled and updates its class’ Q-table. This enables rapid learning as in Wargus there are many units of the same class. We present initial results of CLASS Q-L against a variety of opponents.
- North America > United States > Pennsylvania > Northampton County > Bethlehem (0.04)
- North America > United States > Massachusetts > Middlesex County > Cambridge (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- Europe > Norway > Eastern Norway > Oslo (0.04)
Automatically Generating Game Tactics through Evolutionary Learning
Ponsen, Marc, Munoz-Avila, Hector, Spronck, Pieter, Aha, David W.
The decision-making process of computer-controlled opponents in video games is called game AI. Adaptive game AI can improve the entertainment value of games by allowing computer-controlled opponents to ix weaknesses automatically in the game AI and to respond to changes in human-player tactics. Dynamic scripting is a reinforcement learning approach to adaptive game AI that learns, during gameplay, which game tactics an opponent should select to play effectively. In previous work, the tactics used by dynamic scripting were designed manually. We introduce the evolutionary state-based tactics generator (ESTG), which uses an evolutionary algorithm to generate tactics automatically. Experimental results show that ESTG improves dynamic scripting's performance in a real-time strategy game. We conclude that high-quality domain knowledge can be automatically generated for strong adaptive game AI opponents. Game developers can bene it from applying ESTG, as it considerably reduces the time and effort needed to create adaptive game AI.
- Europe > Netherlands > Limburg > Maastricht (0.05)
- North America > United States > Massachusetts > Middlesex County > Reading (0.04)
- North America > United States > Massachusetts > Middlesex County > Cambridge (0.04)
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