You'd also have to fit the past interactions in the context window of the LLM, otherwise it wouldn't remember them.
Fine-tuning individual agents in order to move memories from the context window to the neural network weights, even if possible, would probably get too expensive.
yeah - so I think this is worth exploring. Given how many tokens you can jam in the prompt even w/ GPT3, I think could do some pretty complex game play, at least compared to what is typical in the lab e.g., I think could easily have it remember how 100 or so other agents behaved in some kind of public goods game.
> Facebook's CICERO artificial intelligence has achieved “human-level performance” in the board game Diplomacy, which is notable for the fact that’s a game built on human interaction, not moves and manoeuvres, like, say, chess.
It was the same scenario - many agents, multiple rounds, complex dialogue based interactions.
That's actually a pretty cool analogy, even the decisionmaking is arguably quite close to how human decision making actually happens (which involves a lot more exchange of words than just transmitting coded information like "accept proposal to exchange X of good Y for Z monetary units"). Might be a bit tricky to get an AI to really "understand" those implications of their response, but it's cool as a thought experiment.
Fine-tuning individual agents in order to move memories from the context window to the neural network weights, even if possible, would probably get too expensive.