Data collection using deep reinforcement learning for serious games

Abstract

Mild Cognitive Impairment (MCI) often occurs prior to the more serious condition of dementia and early detection of MCI is an important but challenging task because of its indistinct symptoms. Work has been done developing serious games on mobile devices for MCI detection as opposed to a typical application of serious games for growing and maintaining mental acuity. The serious games WarCAT and Locker record player’s moves made while playing the game to determine their levels of strategy recognition and learning. To be able to demonstrate this, however, requires a large amount of player data. Therefore, it would be beneficial to develop a method of generating synthetic data that could imitate human player data. The area of machine learning (ML) known as Reinforcement Learning (RL) can be applied to creating a large pool of players since it emulates the way humans learn. In RL, if an action in response to a stimulus is followed by a successful reward, the stimulus-action-reward association will be strengthened, and the reward will be recalled with greater likelihood upon later presentation of the same stimulus and action. Like RL in humans, considerable trial and error (training) is often required. In addition, a growing subfield of machine learning known as Deep Reinforcement Learning uses techniques of Reinforcement Learning along with Artificial Neural Networks for function approximation. The purpose of this thesis is to explore the use of Deep RL to learn to play our serious game and achieve gameplay results comparable to the best human performance. From this, we can define baselines which allow us to create bots with various levels of training to emulate individuals at various stages of learning, or by extension, various levels of cognitive decline.