INTAGS: Interactive Agent-Guided Simulation

ArXiv ID: 2309.01784 “View on arXiv”

Authors: Unknown

Abstract

In many applications involving multi-agent system (MAS), it is imperative to test an experimental (Exp) autonomous agent in a high-fidelity simulator prior to its deployment to production, to avoid unexpected losses in the real-world. Such a simulator acts as the environmental background (BG) agent(s), called agent-based simulator (ABS), aiming to replicate the complex real MAS. However, developing realistic ABS remains challenging, mainly due to the sequential and dynamic nature of such systems. To fill this gap, we propose a metric to distinguish between real and synthetic multi-agent systems, which is evaluated through the live interaction between the Exp and BG agents to explicitly account for the systems’ sequential nature. Specifically, we characterize the system/environment by studying the effect of a sequence of BG agents’ responses to the environment state evolution and take such effects’ differences as MAS distance metric; The effect estimation is cast as a causal inference problem since the environment evolution is confounded with the previous environment state. Importantly, we propose the Interactive Agent-Guided Simulation (INTAGS) framework to build a realistic ABS by optimizing over this novel metric. To adapt to any environment with interactive sequential decision making agents, INTAGS formulates the simulator as a stochastic policy in reinforcement learning. Moreover, INTAGS utilizes the policy gradient update to bypass differentiating the proposed metric such that it can support non-differentiable operations of multi-agent environments. Through extensive experiments, we demonstrate the effectiveness of INTAGS on an equity stock market simulation example. We show that using INTAGS to calibrate the simulator can generate more realistic market data compared to the state-of-the-art conditional Wasserstein Generative Adversarial Network approach.

Keywords: multi-agent system (MAS), agent-based simulator (ABS), reinforcement learning, causal inference, policy gradient, Equity (Stock)

Complexity vs Empirical Score

• Math Complexity: 8.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics including causal inference, reinforcement learning formulation with policy gradients, and stochastic policy optimization, indicating high complexity. It includes extensive experiments on equity stock market simulation, comparing results to state-of-the-art methods, demonstrating substantial empirical rigor.

  flowchart TD
    A["Research Goal"] --> B["Propose MAS Distance Metric"]
    B --> C["Estimate Effects via Causal Inference"]
    C --> D["INTAGS Framework: RL Policy Optimization"]
    D --> E["Policy Gradient Update"]
    E --> F["Calibrate ABS"]
    F --> G["Key Findings: Realistic Equity Simulation"]