Wont this competition just reduce to who can get essentially the most compute and human feedback? More usually, while we enable individuals to make use of, say, simple nested-if strategies, Minecraft worlds are sufficiently random and various that we count on that such methods wont have good performance, especially provided that they have to work from pixels. Wont it take far too long to practice an agent to play Minecraft? 4. Would the GPT-3 for Minecraft approach work effectively for BASALT? Is it sufficient to simply immediate the model appropriately? For example, a sketch of such an strategy would be: – Create a dataset of YouTube videos paired with their robotically generated captions, and train a mannequin that predicts the subsequent video frame from earlier video frames and captions. Train a policy that takes actions which lead to observations predicted by the generative mannequin (effectively studying to mimic human behavior, conditioned on earlier video frames and the caption). We hope that BASALT shall be used by anyone who aims to learn from human feedback, whether they're working on imitation learning, studying from comparisons, or some other method. You may get began now, by merely putting in MineRL from pip and loading up the BASALT environments.