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Real-world tasks of interest are generally poorly defined by human-readabledescriptions and have no pre-defined reward signals unless it is defined by ahuman designer. Conversely, data-driven algorithms are often designed to solvea specific, narrowly defined, task with performance metrics that drives theagent's learning. In this work, we present the solution that won first placeand was awarded the most human-like agent in the 2021 NeurIPS CompetitionMineRL BASALT Challenge: Learning from Human Feedback in Minecraft, whichchallenged participants to use human data to solve four tasks defined only by anatural language description and no reward function. Our approach uses theavailable human demonstration data to train an imitation learning policy fornavigation and additional human feedback to train an image classifier. Thesemodules, combined with an estimated odometry map, become a powerfulstate-machine designed to utilize human knowledge in a natural hierarchicalparadigm. We compare this hybrid intelligence approach to both end-to-endmachine learning and pure engineered solutions, which are then judged by humanevaluators. Codebase is available athttps://github.com/viniciusguigo/kairos_minerl_basalt.

Combining Learning from Human Feedback and Knowledge Engineering to Solve Hierarchical Tasks in Minecraft