Adaptive Playing & Opponent Modeling in Competitive Games

Link to the code

Abstract

Despite the success of Artificial Intelligence systems such as Deep Blue, AlphaGo and OpenAI five, most modern commercial videogames still rely on the use of scripts. This is mainly because developers are concerned about the unpredictable behavior of AI.

The ability to create a balanced matching between the user’s skills and the level of difficulty of the game can greatly improve the user’s gaming experience.

Previous attempts at achieving this goal have required a large amount of data from human matches or specific knowledge related to the game.

In our work, we have proposed a new method that allows us to eliminate the demand for data from games between human players and to decrease the amount of specific knowledge related to the game needed.

Train and Store

In this section we train an
agent through
reinforcement learning and
self-play tecniques.
During this phase we
evaluate the agent and
store some of its best
policies for the next step.

Adaptive Playing

We use the previously stored
policies to create a dataset
of matches between agents
using these policies.
We train a model to recognize
the level of the agents by
looking at their sequence of moves.