Most existing benchmarks for grounding language in interactive environments either lack realistic linguistic elements, or prove difficult to scale up due to substantial human involvement in the collection of data or feedback signals. We develop WebShop – a simulated e-commerce website environment with 1.18 million real-world products and 12,087 crowd-sourced text instructions. In this environment, an agent needs to navigate multiple types of webpages and issue diverse actions to find, customize, and purchase a product given an instruction. WebShop provides several challenges including understanding compositional instructions, query (re-)formulation, dealing with noisy text in webpages, and performing strategic exploration. We collect over 1,600 human trajectories to first validate the benchmark, then train and evaluate a diverse range of agents using reinforcement learning, imitation learning, and pre-trained image and language models. Our best model achieves a task success rate of 29%, which significantly outperforms rule heuristics but is far lower than expert human performance (59%). We also analyze agent and human trajectories and ablate various model components to provide insights for developing future agents with stronger language understanding and decision making abilities. Finally, we show our agent trained on WebShop exhibits non-trivial sim-to-real transfer when evaluated on amazon.com and ebay.com, indicating the potential value of our benchmark for developing practical web agents that can operate in the wild.
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This content will become publicly available on July 1, 2025
Agent-based crowd simulation: an in-depth survey of determining factors for heterogeneous behavior
In recent years, the field of crowd simulation has experienced significant advancements, attributed in part to the improvement of hardware performance, coupled with a notable emphasis on agent-based characteristics. Agent-based simulations stand out as the preferred methodology when researchers seek to model agents with unique behavioral traits and purpose-driven actions, a crucial aspect for simulating diverse and realistic crowd movements. This survey adopts a systematic approach, meticulously delving into the array of factors vital for simulating a heterogeneous microscopic crowd. The emphasis is placed on scrutinizing low-level behavioral details and individual features of virtual agents to capture a nuanced understanding of their interactions. The survey is based on studies published in reputable peer-reviewed journals and conferences. The primary aim of this survey is to present the diverse advancements in the realm of agent-based crowd simulations, with a specific emphasis on the various aspects of agent behavior that researchers take into account when developing crowd simulation models. Additionally, the survey suggests future research directions with the objective of developing new applications that focus on achieving more realistic and efficient crowd simulations.
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- Award ID(s):
- 2005430
- PAR ID:
- 10532347
- Publisher / Repository:
- Springer
- Date Published:
- Journal Name:
- The Visual Computer
- Volume:
- 40
- Issue:
- 7
- ISSN:
- 0178-2789
- Page Range / eLocation ID:
- 4993 to 5004
- Subject(s) / Keyword(s):
- Crowd simulation Autonomous agents Psychological models Microscopic models Multi-agent simulation
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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