Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Managing multiple agents by automatically adjusting incentives

Shunichi Akatsuka, Yaemi Teramoto, Aaron Courville

TL;DR

This work addresses coordinating self-interested agents in general-sum settings by introducing a manager agent that mediates interactions through auxiliary states and incentives. The manager aims to maximize the aggregate agent rewards while minimizing payout, effectively shaping individual policies toward collective welfare. In a supply-chain MARL testbed, the manager-enhanced framework improves average factory rewards by about 23.8%, increases the manager's reward by roughly 20.1%, and boosts raw rewards by around 22.2% compared with a naive MARL baseline, partly by shifting purchasing from a cheaper, low-capacity supplier to a higher-capacity option to meet on-time demand. Overall, the approach demonstrates a scalable automated mechanism design strategy for promoting societal welfare in multi-agent decision problems, while acknowledging limitations related to agent sophistication and architecture interactions.

Abstract

In the coming years, AI agents will be used for making more complex decisions, including in situations involving many different groups of people. One big challenge is that AI agent tends to act in its own interest, unlike humans who often think about what will be the best for everyone in the long run. In this paper, we explore a method to get self-interested agents to work towards goals that benefit society as a whole. We propose a method to add a manager agent to mediate agent interactions by assigning incentives to certain actions. We tested our method with a supply-chain management problem and showed that this framework (1) increases the raw reward by 22.2%, (2) increases the agents' reward by 23.8%, and (3) increases the manager's reward by 20.1%.

Managing multiple agents by automatically adjusting incentives

TL;DR

This work addresses coordinating self-interested agents in general-sum settings by introducing a manager agent that mediates interactions through auxiliary states and incentives. The manager aims to maximize the aggregate agent rewards while minimizing payout, effectively shaping individual policies toward collective welfare. In a supply-chain MARL testbed, the manager-enhanced framework improves average factory rewards by about 23.8%, increases the manager's reward by roughly 20.1%, and boosts raw rewards by around 22.2% compared with a naive MARL baseline, partly by shifting purchasing from a cheaper, low-capacity supplier to a higher-capacity option to meet on-time demand. Overall, the approach demonstrates a scalable automated mechanism design strategy for promoting societal welfare in multi-agent decision problems, while acknowledging limitations related to agent sophistication and architecture interactions.

Abstract

In the coming years, AI agents will be used for making more complex decisions, including in situations involving many different groups of people. One big challenge is that AI agent tends to act in its own interest, unlike humans who often think about what will be the best for everyone in the long run. In this paper, we explore a method to get self-interested agents to work towards goals that benefit society as a whole. We propose a method to add a manager agent to mediate agent interactions by assigning incentives to certain actions. We tested our method with a supply-chain management problem and showed that this framework (1) increases the raw reward by 22.2%, (2) increases the agents' reward by 23.8%, and (3) increases the manager's reward by 20.1%.
Paper Structure (10 sections, 8 equations, 7 figures, 2 tables)

This paper contains 10 sections, 8 equations, 7 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Proposed Method
  3. General Multi-Agent Reinforcement Learning (MARL)
  4. MARL with a manager
  5. Experiments
  6. The supply-chain optimization problem
  7. RL formalization
  8. Training and Evaluation
  9. Results
  10. Conclusion and Discussion

Figures (7)

  • Figure 1: Multi-agent reinforcement learning with a manager.
  • Figure 2: Visualization of the supply chain in our experiments.
  • Figure 3: Visualization of an environment step. An environment step consists of seven days.
  • Figure 5: Naïve
  • Figure 6: With Manager
  • ...and 2 more figures