Reading Literature

Designing a reinforcement learning-based adaptive AI for large-scale strategy games

Application of reinforcement learning to large-scale strategy games.

An important goal of the game AI, i.e., to continuously challenge the user at his level, can be easily missed, jeopardizing the game replay value.

One of the most important features of strategic decision-making is the capability to adapt and learn.

Is machine learning applicable to large-scale strategy games? Can it lead to an AI with higher performance? Can this be done in reasonable time?

Reinforcement Learning:

- There is no supervisor.

- It is based on the principle of trial and error.

- It is guided by an anticipated estimate reward that agrees to desired results ( a fixed goal, without requiring a direct description of the agent behavior).

Sarsa Algorithm

Game: Battleground