Automatic game-testing with personality : Multi-task reinforcement learning for automatic game-testing

Sammanfattning: This work presents a scalable solution to automate game-testing. Traditionally, game-testing has been performed by either human players or scripted Artificial Intelligence (AI) agents. While the first produces the most reliable results, the process of organizing testing sessions is time consuming. On the other hand, scripted AI dramatically speeds up the process, however, the insights it provides are far less useful: these agents’ behaviors are highly predictable. The presented solution takes the best of both worlds: the automation of scripted AI, and the richness of human testing by framing the problem within the Deep Reinforcement Learning (DRL) paradigm. Reinforcement Learning (RL) agents are trained to adapt to any unseen level and present customizable human personality traits: such as aggressiveness, greed, fear, etc. This is achieved exploring the problem from a multi-task RL setting. Each personality trait is understood as a different task which can be linearly combined by the proposed algorithm. Furthermore, since Artificial Neural Networks (ANNs) have been used to model the agent’s policies, the solution is highly adaptable and scalable. This thesis reviews the state of the art in both automatic game-testing and RL, and proposes a solution to the above-mentioned problem. Finally, promising results are obtained evaluating the solution on two different environments: a simple environment used to quantify the quality of the designed algorithm, and a generic game environment useful to show-case its applicability. In particular, results show that the designed agent is able to perform good on game levels never seen before. In addition, the agent can display any convex combination of the trained behaviors. Furthermore, its performance is as good as if it had been specifically trained on that particular combination.