Vanquish Your Past: Shifted Imitation Learning in Hades

Abstract

Contemporary video games offer a plethora of interesting challenges, yet the application of imitation learning in shipped titles remains sparse. One reason is that directly mirroring a player’s setup and behavior can lead to a stale or unfair combat experience where only computer-reaction time or endurance wins the battle. To address this, we investigate a temporally shifted generative imitation learning approach to enhance player-specific adaptation and implicit dynamic difficulty adjustment (DDA). By shifting the imitation model by one iteration of a playthrough, we create an asynchronous confrontation that requires players to think strategically both forwards and backwards in time. Players must make delicate trade-offs: polishing their current build to master the run, while avoiding overtuning an agent they must face in the future. We implemented this in “Dark Zagreus”, a modification of the action roguelike Hades, replacing the final boss with an AI trained on the player’s last successful run. To evaluate its effectiveness, we conducted a two-week user study with 20 participants comparing scripted and imitation-based behaviors. While quantitative metrics showed no significant differences in overall player experience, qualitative feedback highlighted that the AI effectively mirrored individual tactics, and performance analyses revealed accuracy comparable to prior imitation learning approaches. Despite limitations such as omitted game mechanics, this work demonstrates the potential of shifted imitation learning to realize novel, engaging gameplay experiences. The source code is publicly released to foster future research on player-centric AI design.