A2Perf: Real-World Autonomous Agents Benchmark

Abstract: Autonomous agents and systems cover a number of application areas, from robotics and digital assistants to combinatorial optimization, all sharing common, unresolved research challenges. It is not sufficient for agents to merely solve a given task; they must generalize to out-of-distribution tasks, perform reliably, and use hardware resources efficiently during training and inference, among other requirements. Several methods, such as reinforcement learning and imitation learning, are commonly used to tackle these problems, each with different trade-offs. However, there is a lack of benchmarking suites that define the environments, datasets, and metrics which can be used to provide a meaningful way for the community to compare progress on applying these methods to real-world problems. We introduce A2Perf--a benchmark with three environments that closely resemble real-world domains: computer chip floorplanning, web navigation, and quadruped locomotion. A2Perf provides metrics that track task performance, generalization, system resource efficiency, and reliability, which are all critical to real-world applications. Using A2Perf, we demonstrate that web navigation agents can achieve latencies comparable to human reaction times on consumer hardware, reveal reliability trade-offs between algorithms for quadruped locomotion, and quantify the energy costs of different learning approaches for computer chip-design. In addition, we propose a data cost metric to account for the cost incurred acquiring offline data for imitation learning and hybrid algorithms, which allows us to better compare these approaches. A2Perf also contains several standard baselines, enabling apples-to-apples comparisons across methods and facilitating progress in real-world autonomy. As an open-source benchmark, A2Perf is designed to remain accessible, up-to-date, and useful to the research community over the long term.

• The paper introduces A2Perf’s novel benchmark that evaluates autonomous agents using metrics for generalization, resource efficiency, and reliability.
• It assesses performance across diverse domains like chip floorplanning, web navigation, and quadruped locomotion, emphasizing real-world applicability.
• Findings reveal that approaches such as PPO provide consistent performance while the innovative data cost metric ensures fair algorithm comparisons.

A2Perf: Real-World Autonomous Agents Benchmark

The paper "A2Perf: Real-World Autonomous Agents Benchmark" introduces a novel benchmarking suite designed for evaluating autonomous agents across critical real-world domains. This article provides a comprehensive overview of A2Perf, detailing its structure, methodology, and implications for future research in autonomous systems.

Introduction and Motivation

A2Perf addresses the pressing need for standardized benchmarks that can evaluate autonomous agents across various real-world scenarios, such as computer chip floorplanning, web navigation, and quadruped locomotion. Existing methods like reinforcement learning (RL) and imitation learning (IL) face challenges of generalization, reliability, and resource efficiency, which are not fully captured by current benchmarking suites. A2Perf fills this gap by providing metrics that encompass task performance, generalization, system resource efficiency, and reliability—all vital for practical applications in the autonomous agents space.

Figure 1: The three domains included in A2Perf: computer chip floorplanning for optimizing integrated circuit layouts, web navigation for automated form filling and website interaction, and quadruped locomotion for robotic control. These specific domains were selected based on their demonstrated transfer from simulation to real-world applications.

Benchmark Structure

Domains and Metrics

A2Perf comprises three domains: computer chip floorplanning, web navigation, and quadruped locomotion. These domains were selected due to their industrial relevance and the ability to transfer learning outcomes from simulated to real-world applications effectively. Each domain is supported by metrics that assess:

• Generalization: Agents' ability to apply learned skills to new, unseen tasks.
• System Resource Efficiency: Evaluation of computational resources consumed during training and inference.
• Reliability: Consistency and predictability of performance across different scenarios.
• Data Cost: The energy and resources required for data collection, especially crucial for imitation learning scenarios.

Data and Performance Metrics

The benchmark includes an innovative data cost metric, which assesses the energy required to generate training datasets. This allows fair comparison across approaches with varying levels of reliance on pre-collected data. For instance, in the chip floorplanning domain, the data cost for behavioral cloning was significantly higher than online reinforcement methods, highlighting the importance of considering data acquisition efforts in evaluating learning algorithms.

Evaluation and Insights

The evaluation section of the paper demonstrates A2Perf's capacity to provide deep insights into the operational characteristics of autonomous agents. For example, in the web navigation domain, trained agents achieved latencies comparable to human reaction times, indicating the potential for real-time deployment on consumer hardware (Figure 2).

Figure 2: Comparison of web navigation agent latency with human reaction time. Agents are fast enough for real-time form-filling tasks, even when served from the cloud.

In the quadruped locomotion domain, reliability metrics revealed that proximal policy optimization (PPO) algorithms provided more consistent and reliable performance compared to other reinforcement learning strategies, underscoring the critical role of reliability metrics in real-world deployments.

Implications and Future Directions

A2Perf represents a significant advancement in the benchmarking of autonomous agents. Its ability to measure multi-dimensional aspects of agents' capabilities makes it a vital tool for researchers and engineers aiming to deploy such systems in practical settings. The authors encourage the research community to contribute to the continual development of A2Perf, suggesting expansions into multi-agent and additional domain tasks. Such developments could include extending benchmarks to assess coordination and interaction dynamics in multi-agent settings, which are increasingly relevant in domains like automated driving and collaborative robotics.

Conclusion

A2Perf ushers in a new era of benchmarking autonomous agents, highlighting the multidimensional nature of real-world applicability. Through rigorous evaluation metrics and carefully selected domains, A2Perf provides a foundational tool for refining and deploying autonomous systems across industries. Its open-source nature and extensibility ensure it will remain a cornerstone for future advancements in autonomous agent technologies.