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Exploring Expertise through Visualizing Agent Policies and Human Strategies in Open-Ended Games

In this research, we explore the problem solving strategies of both humans and AI agents in the open-ended domain of video games. We utilize data collected from several human-level performing AI agents, that follow a given policy, and data from expert human players, that follow a set of strategies, for two Atari 2600 console games. We compare both types of data streams using a visualization technique to gain insights about how each player type, AI or expert human, go about solving the given games. Analyzing the action sequences of the two, we demonstrate how closely the agent policies resemble the real-world problem solving of a human player, and explore how we might extract human-level strategies for agent policies. We reflect on the benefits of using data from both AI agents and expert humans to instruct learners, model their behaviour, and how strategies may be more apparent and easier to adopt from human play. Finally, we hypothesize the benefits of combining both types of data for learning these complex tasks within open-ended domains.