Game AI Research with Fast Planet Wars Variants

© 2018 IEEE. This paper describes a new implementation of Planet Wars, designed from the outset for Game AI research. The skill-depth of the game makes it a challenge for game-playing agents, and the speed of more than 1 million game ticks per second enables rapid experimentation and prototyping. The parameterised nature of the game together with an interchangeable actuator model make it well suited to automated game tuning. The game is designed to be fun to play for humans, and is directly playable by General Video Game AI agents

Weighting NTBEA for Game AI Optimisation

The N-Tuple Bandit Evolutionary Algorithm (NTBEA) has proven very effective in optimising algorithm parameters in Game AI. A potential weakness is the use of a simple average of all component Tuples in the model. This study investigates a refinement to the N-Tuple model used in NTBEA by weighting these component Tuples by their level of information and specificity of match. We introduce weighting functions to the model to obtain Weighted- NTBEA and test this on four benchmark functions and two game environments. These tests show that vanilla NTBEA is the most reliable and performant of the algorithms tested. Furthermore we show that given an iteration budget it is better to execute several independent NTBEA runs, and use part of the budget to find the best recommendation from these runs

Mustang Daily, February 4, 1971

Student newspaper of California Polytechnic State University, San Luis Obispo, CA.https://digitalcommons.calpoly.edu/studentnewspaper/2642/thumbnail.jp

Advancements in Safe Deep Reinforcement Learning for Real-Time Strategy Games and Industry Applications

publishedVersio