Warning
The videos on this page represent the v0.4 dataset, not the v1.0 dataset.
In the Benchmark for Agents that Solve Almost-Lifelike Task (BASALT) competition, your task is to solve tasks based on human judgement, instead of pre-defined reward functions. The goal is to produce agents that are judged by real humans to be effective at solving a given task. This calls for training on human-feedback, whether it is training from demonstrations, training on human preferences or using humans to correct agents’ actions.
from minerl.herobraine.env_specs.basalt_specs import FindCaveEnvSpec, PenAnimalsVillageEnvSpec, MakeWaterfallEnvSpec, VillageMakeHouseEnvSpec env_specs = [FindCaveEnvSpec, PenAnimalsVillageEnvSpec, MakeWaterfallEnvSpec, VillageMakeHouseEnvSpec] from minerl.utils.documentation import print_env_spec_sphinx for env_spec in env_specs: print_env_spec_sphinx(env_spec)