glmcdona / LuxPythonEnvGym

Matching python environment code for Lux AI 2021 Kaggle competition, and a gym interface for RL models.
MIT License
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[Discussion] Support mix of hardcoded and RL logic #79

Closed glmcdona closed 2 years ago

glmcdona commented 2 years ago

Just noting here, that the winning imitation learning solution only applies actions to Units, not CityTiles: https://www.kaggle.com/shoheiazuma/lux-ai-with-imitation-learning

The cities have hardcoded actions of building workers if possible, otherwise research. Adding support for this would approximately reduce the number of action decisions by ~33%, and reduce the number of actions by a couple.

Suggest we add more agent callbacks for hardcoded logic to apply that can give units actions without creating an observation. Thoughts? I think we can make it backwards compatible.

royerk commented 2 years ago

I wanted to try the idea when I saw that code but I don't know how to add such a callback to use rules in certain conditions. (Could be used when stacked units all try to go in the same direction too)

On Thu, Sep 23, 2021, 21:54 Geoff McDonald @.***> wrote:

Just noting here, that the winning imitation learning solution only applies actions to Units, not CityTiles: https://www.kaggle.com/shoheiazuma/lux-ai-with-imitation-learning

The cities have hardcoded actions of building workers if possible, otherwise research. Adding support for this would approximately reduce the number of action decisions by ~33%, and reduce the number of actions by a couple.

Suggest we add more agent callbacks for hardcoded logic to apply that can give units actions without creating an observation. Thoughts? I think we can make it backwards compatible.

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