# Playing a Strategy Game with Knowledge-Based Reinforcement Learning

## TL;DR

This paper introduces Knowledge-Based Reinforcement Learning (KB-RL), combining expert knowledge and RL to solve complex strategy games, demonstrating improved performance and learning over time in the FreeCiv game.

## Contribution

The paper presents a novel KB-RL approach that integrates multi-expert knowledge with reinforcement learning for complex problem solving in strategy games.

## Key findings

- KB-RL can successfully play and complete FreeCiv.
- The system improves with more games, reducing the number of rounds to win.
- KB-RL outperforms baseline strategies in various settings.

## Abstract

This paper presents Knowledge-Based Reinforcement Learning (KB-RL) as a method that combines a knowledge-based approach and a reinforcement learning (RL) technique into one method for intelligent problem solving. The proposed approach focuses on multi-expert knowledge acquisition, with the reinforcement learning being applied as a conflict resolution strategy aimed at integrating the knowledge of multiple exerts into one knowledge base. The article describes the KB-RL approach in detail and applies the reported method to one of the most challenging problems of current Artificial Intelligence (AI) research, namely playing a strategy game. The results show that the KB-RL system is able to play and complete the full FreeCiv game, and to win against the computer players in various game settings. Moreover, with more games played, the system improves the gameplay by shortening the number of rounds that it takes to win the game. Overall, the reported experiment supports the idea that, based on human knowledge and empowered by reinforcement learning, the KB-RL system can deliver a strong solution to the complex, multi-strategic problems, and, mainly, to improve the solution with increased experience.