[P] I trained an AI to play Resident Evil 4 Remake using Behavioral Cloning + LSTM

A developer has successfully trained an AI agent to navigate the intense combat of Resident Evil 4 Remake using a technique called Behavioral Cloning. By recording their own gameplay footage—capturing actions like running, shooting, reloading, and dodging enemies in the game's opening village section—they created a dataset to teach a model to mimic human decision-making. To move beyond simple frame-by-frame reactions, the researcher incorporated a Long Short-Term Memory (LSTM) layer, allowing the AI to maintain context and memory across sequential time steps, which is crucial for coherent gameplay.

The results were revealing: the cloned AI agent could competently handle one-on-one encounters, effectively replicating the recorded combat maneuvers. However, its performance broke down in more chaotic, multi-enemy situations. The model struggled with the nuanced 'fight-or-flight' decisions required when surrounded, a complexity that the initial imitation data failed to fully capture. This gap underscores a fundamental challenge in AI training: while Behavioral Cloning is powerful for learning explicit skills, it often falls short when agents need to generalize to novel, high-pressure scenarios not present in the training data. The full experiment, including source code and Jupyter notebooks, has been shared openly on GitHub, providing a valuable case study for the game AI and machine learning communities.