Thanks, your post saved me a lot of time. I was also interested in using XGBoost as model in RL, so I started looking for options to implement that. I was very surprised when I read in your post that GBTs can not be trained partially/incrementally. So I did some research. Short version (summary from hcho3 answer here) — it’s basically a fundamental limitation of GBT algo itself, not specific lib (XGBoost/LightGBM/etc). Tree construction require whole data at the beginning to create optimal splits. One way to kinda do this (in very limited way), is `process_type: update`, but it will only modify leaves, not the tree structure. Which basically means no go for RL, because you need good initial starting point. Some discussion here also.

Creator of https://tradinggym.app | FullStack Dev | {Algo}Trader | https://twitter.com/___deandree

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