This is a particularly important property for a benchmark where the point is to determine what to do: it signifies that human feedback is vital in identifying which activity the agent should carry out out of the many, many duties which might be possible in precept. Certainly, it often doesnt capture what we would like, with many latest examples displaying that the supplied specification usually leads the agent to behave in an unintended way. Since we cant anticipate a good specification on the first attempt, a lot latest work has proposed algorithms that instead enable the designer to iteratively talk details and preferences about the task. You can not, nevertheless, use mods, custom JARs, you dont get instant setup, day by day backups, and you cant take away Server.pros ads from your server. In fact, in reality, tasks dont come pre-packaged with rewards; those rewards come from imperfect human reward designers. When testing your algorithm with BASALT, you dont have to worry about whether or not your algorithm is secretly learning a heuristic like curiosity that wouldnt work in a extra real looking setting.