I’ve been working on training an agent to play a segment of Resident Evil Requiem , focusing on a fast-paced, semi-linear escape sequence with enemies and time pressure. Instead of going fully reinforcement learning from scratch, I used a hybrid approach: Behavior Cloning (BC) for initial policy learning from human demonstrations HG-DAgger to iteratively improve performance and reduce compounding errors The environment is based on gameplay capture, where I map controller inputs into a discretized action space. Observations are extracted directly from frames (with some preprocessing), and the agent learns to mimic and then refine behavior over time. One of the main challenges was the instability early on — especially when the agent deviates slightly from the demonstrated trajectories (classic BC issue). HG-DAgger helped a lot by correcting those off-distribution states. Another tricky part was synchronizing actions with what’s actually happening on screen, since even small timing mismatches can completely break learning in this kind of game. After training, the agent is able to: Navigate the sequence consistently React to enemies in real time Recover from small deviations (to some extent) I’m still experimenting with improving robustness and generalization (right now it’s quite specialized to this segment). Happy to share more details (training setup, preprocessing, action space, etc.) if anyone’s interested. submitted by /u/AgeOfEmpires4AOE4
Originally posted by u/AgeOfEmpires4AOE4 on r/ArtificialInteligence