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Existing benchmarks for grounding language in interactive environments eitherlack real-world linguistic elements, or prove difficult to scale up due tosubstantial human involvement in the collection of data or feedback signals. Tobridge this gap, we develop WebShop -- a simulated e-commerce websiteenvironment with $1.18$ million real-world products and $12,087$ crowd-sourcedtext instructions. Given a text instruction specifying a product requirement,an agent needs to navigate multiple types of webpages and issue diverse actionsto find, customize, and purchase an item. WebShop provides several challengesfor language grounding including understanding compositional instructions,query (re-)formulation, comprehending and acting on noisy text in webpages, andperforming strategic exploration. We collect over $1,600$ human demonstrationsfor the task, and train and evaluate a diverse range of agents usingreinforcement learning, imitation learning, and pre-trained image and languagemodels. Our best model achieves a task success rate of $29\%$, whichoutperforms rule-based heuristics ($9.6\%$) but is far lower than human expertperformance ($59\%$). We also analyze agent and human trajectories and ablatevarious model components to provide insights for developing future agents withstronger language understanding and decision making abilities. Finally, we showthat agents trained on WebShop exhibit non-trivial sim-to-real transfer whenevaluated on amazon.com and ebay.com, indicating the potential value of WebShopin developing practical web-based agents that can operate in the wild.

WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents