For example, a recent study [1] concludes that NPCs based on behavior trees (BTs) are still more viable than those based on machine learning (ML), calling for new approaches, strategies, and tooling to overcome the barrier to adoption. Additional work has also underscored the need for reusable and adjustable models [2], motivated by game developers' preferences to reuse previously developed assets, provided that reuse does not result in repetitive gameplay. Traditional BT approaches and modern RL techniques each have their respective strengths and limitations in video game development. BTs offer a structured and hierarchical method for managing NPC behaviors, enabling the design of complex systems with predictable outcomes given sufficient development time. However, this complexity can make multi-task BTs less engaging and cumbersome to develop [2]. Conversely, RL provides a dynamic and adaptive approach to decision making [3], allowing developers to guide an agent through trial-and-error. However, training generally-capable RL models remains a challenge, particularly due to reward shaping, negative task transfer [4, 5], and compute resource demands [6].

We suggest a simple practical method to combine the human and artificial intelligence to both learn best investment practices of fund managers, and provide recommendations to improve them. Our approach is based on a combination of Inverse Reinforcement Learning (IRL) and RL. First, the IRL component learns the intent of fund managers as suggested by their trading history, and recovers their implied reward function. At the second step, this reward function is used by a direct RL algorithm to optimize asset allocation decisions. We show that our method is able to improve over the performance of individual fund managers.

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