RL project on Monster Hunter Tri: struggling with partial observability and unstable monster state
Hello everyone,
I’m building a RL project around Monster Hunter Tri running in Dolphin, and I’m hitting a set of problems that feel very close to partial observability / state estimation rather than “just” policy learning.
The setup is hybrid:
– memory reads for player state and environment context,
– heuristic detection when memory is incomplete,
– an octree/cube-based spatial approximation,
– and eventually more vision-based signals.
The biggest issue is monster state. I can get some usable information for the player, but monsters are much harder:
– small monsters have readable HP, but their positions are unreliable,
– the same HP addresses can remain present across zones, so I had to build extra conditions to verify whether a monster is actually present,
– and for large monsters I currently do not have a reliable address at all.
So the hard part is not just control, it is learning under noisy, incomplete, and sometimes stale observations. I’m also planning to condition the policy on weapon identity and weapon type instead of hardcoding, so I’m especially interested in methods that would help with:
– POMDP-style learning,
– latent state inference,
– multimodal observation fusion,
– and conditioning a policy on equipment / weapon embeddings.
If anyone has suggestions, papers, or design patterns for this kind of setup, I’d be very grateful.
submitted by /u/Primary_Cheesecake63
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