Evidence receipt / evaluation
Published · transcript-backedKanjun Qiu: evaluation
14 Oct 2023 Latent Space Why AI Agents Don't Work (yet) - with Kanjun Qiu of Imbue
“At that time we built Avalon as a research environment to help us learn particular things.”
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Everything needed to verify it.
- Speaker
- Kanjun Qiu
- Attribution
- Verified speaker
- Claim type
- evaluation
- Recorded
- 14 Oct 2023
- Publisher
- Latent Space
Transcript context
…That's above my pay grade. But Avalon is like a hundred times faster than Minecraft for simulation. When did you figure that out that you needed to just like build your own thing? Was it kind of like your engineering team was like, Hey, this is too slow. Was it more a long-term investment? Yeah. At that time we built Avalon as a research environment to help us learn particular things. And one thing we were trying to learn is like, how do you get an agent that is able to do many different tasks? Like RL agents at that time and environments at that time. What we heard from other RL researchers was the like biggest thing keeping holding the field back is lack of benchmarks that let us explore things like planning and curiosity and things like that and have the agent actually perform better if the agent has curiosity. And so we were trying to figure out in a situation where, how can we have agents that are able to handle lots of different types of tasks without the reward being pretty handcrafted? That's a lot of what we had seen is that like these very handcrafted rewards. And so Avalon has like a single reward it's across all tasks. And it also allowed us to create a curriculum so we could make the level more or less difficult. And it taught us a lot, maybe two primary things. One is with no curriculum, RL algorithms don't work at all. So that's actually really interesting. For the non RL specialists, what is a curriculum in your terminology?…
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