[itchyjunk]

Since diamonds are surrounded by danger and if it dies, it loses its items and such, why would it not be satisfied after discovering iron pick axe or somesuch? Is it in a mode where it doesn't lose its item when it dies? Does it die a lot? Does it ever try digging vertically down? Does it ever discover other items/tools you didn't expect it to? Open world with sparse reward seems like such a hard problem. Also, once it gets the item, does it stop getting reward for it? I assume so. Surprised that it can work with this level of sparse rewards.

[taneq]

In all reinforcement learning there is (explicitly as part of a fitness function, or implicitly as part of the algorithm) some impetus for exploration. It might be adding a tiny reward per square walked, a small reward for each block broken and a larger one for each new block type broken. Or it could be just forcing a random move every N steps so the agent encounters new situations through “clumsiness”.

[kevindamm]

That is right, there is usually a parameter on the action selection function -- the exploitation vs exploration balance.

[danijar]

When it dies it loses all items and the world resets to a new random seed. It learns to stay alive quite well but sometimes falls into lava or gets killed by monsters.

It only gets a +1 for the first iron pickaxe it makes in each world (same for all other items), so it can't hack rewards by repeating a milestone.

Yeah it's surprising that it works from such sparse rewards. I think imagining a lot of scenarios in parallel using the world model does some of the heavy lifting here.

[SpaceManNabs]

> Yeah it's surprising that it works from such sparse rewards. I think imagining a lot of scenarios in parallel using the world model does some of the heavy lifting here.

This is such gold. Thanks for sharing. Immediately added to my notes.