How to design a revenue-maximizing auction where multiple bidders are incentivized to report their values truthfully? And how to handle discontinuous utility functions that arise when agents best-respond in contract and persuasion settings?

We introduce GEneral Menu-based NETwork (GemNet), the first strategy-proof and general auction that supports multiple bidders. &nbsp; We hope GemNet can shed light on theoretical analysis: it shows that multi-bidder auction designs can be similar to analytically optimal designs of their single-bidder counterparts. See figures below.

Our method reproduces the optimal auction in the setting with 2 additive bidders, 2 items, and valuations on support size two [Yao, 2017].

Economics assumes that agents are best responding. When best responses change, utilities may change discontinuously. We develop discontinuous networks to handle these cases. &nbsp; Designed for contract design, where the utility function is piecewise linear but discontinuous. [NeurIPS 2023] &nbsp; For the figure below: (a) The exact surface of the principal's utility function. (b) A learned ReLU network cannot model the discontinuity and yields an incorrect contract. (c) A learned DeLU network represents a discontinuous function and well-approximates the ground-truth.

Designed for multi-sender persuasion, where the utility function is piecewise non-linear and discontinuous. [ICML 2024] &nbsp; In each column below, we show the ground-truth principal's utility and the approximation results achieved by our method, ReLU, and piecewise linear discontinuous networks, respectively.

AI for Economics

Multi-agent systems can model both cooperative/competitive agents and the joints of a robot — where each joint is itself an agent. Below are research directions spanning both settings.

Related publications: [GA-SPP, AAMAS 2019] GA-SPP is the first gradient-ascent algorithm with a finite learning rate that provides convergence guarantee in general-sum games, including m×n positive semi-definite games and 2×n general-sum games.

Related publications: [TRAM, NeurIPS Workshop 2022] We study how to improve the robustness of multi-agent learning algorithms by attacking them during training time.

Related publications: [MATTAR, NeurIPS Workshop 2022] How to transfer the policy learned by one multi-agent team to another?

A human central nervous system decreases the control complexity by producing a small number of electrical signals and activating muscles in groups. We achieve this in robots. [NeurIPS 2022] &nbsp; The following figure shows the synergy structure evolution enabled by our method. Phases are divided according to changes in synergy clusters. Synergy clusters are masked with colored shapes.

How to learn an effective morphology for a specific task that is symmetric? [ICML 2023] &nbsp; The following figure shows the morphology evolution enabled by our method.

Related publications: [ROMA, ICML 2020; RODE, ICLR 2021] We developed role-based learning, where agents learn their roles to decompose a complex task. In the figure below, we show changes in roles of different agents in an episode. The role will decide the agent's behavior.

Related publications: [NDQ, ICLR 2020; Pragmatic Reasoning Communication, NeurIPS 2020] We studied sparse, concise, but informative communication. In the following figure, two agents start at a_4 and b_3, respectively, and they want to reach g simultaneously but can only observe its own state. To finish the task, they need to communicate their location to each other. The second row shows our communication strategy. 0 means no communication. Agents learn to only send a bit when they are one step away from the goal state.

Related publications: [CDS, NeurIPS 2021] We find that diversity matters in multi-agent collaboration. The following figure shows that our method learns versatile strategies by encouraging diversity in the difficult Google football tasks.

Related publications: [NLCG, NeurIPS 2022; SOP-CG, ICML 2022; CASEC, ICLR 2022] Coordination graphs: sparse, non-linear, and self-organized. The following figure shows how the coordination structure could be adaptive: (a) Self-organized grouping at initialization; (b) Connecting to agent with rich observation for better information sharing; (c) Concentrated collaboration structure around an enclosed adversary.

Related publications: [EDTI, ICLR 2020] We find that encouraging agents' influence on each other can encourage exploration in large observation-action spaces.

Related publications: [DOP, ICLR 2020] We developed a multi-agent policy gradient method with significantly reduced variance.

Research Projects

AI for Economics

Multi-Agent/Robot