We appreciate R1's recognition of the novelty of our contribution to MARL and the potential impact on a We address R1's two concerns below. "give-reward" actions are direct applications of conventional RL (which have been applied to multi-agent incentivization We appreciate R2's positive feedback on our quantitative results and we are glad that our behavioral Figure 6b where the agent gives nonzero reward for "fire cleaning beam but miss" after 40k steps, one reason is that the Figure 6a), so it may have "forgotten" the difference between successful and unsuccessful usage of the cleaning beam. As demonstrated more clearly in the Escape Room results (e.g. We thank R3 for recognizing our contribution to the general class of opponent-shaping algorithms. Prisoner's Dilemma is fully observable).

Large-scale simulation models of complex socio-technical systems provide decision-makerswith high-fidelity testbeds inwhich policyinterventions canbe evaluated andwhat-if scenarios explored.

The social network for AI bots resembled a spectator battle, with AI enthusiasts competing to make their agents look sentient. Lots of influential people in tech last week were describing Moltbook, an online hangout populated by AI agents interacting with one another, as a glimpse into the future. It appeared to show AI systems doing useful things for the humans that created them (one person used the platform to help him negotiate a deal on a new car). Sure, it was flooded with crypto scams, and many of the posts were actually written by people, but about it pointed to a future of helpful AI, right? The whole experiment reminded our senior editor for AI, Will Douglas Heaven, of something far less interesting: Pokémon. Back in 2014, someone set up a game of Pokémon in which the main character could be controlled by anyone on the internet via the streaming platform Twitch.

In collaborative machine learning(CML), multiple agents pool their resources (e.g.,data)together foracommon learning task.

In collaborative machine learning(CML), multiple agents pool their resources (e.g.,data)together foracommon learning task.