A scalable, simulated web environment with real-world data for developing grounded language agents


Existing benchmarks for grounding language in interactive environments either lack real-world linguistic elements, or prove difficult to scale up due to substantial human involvement in the collection of data or feedback signals. To bridge this gap, we develop WebShop – a simulated e-commerce website environment with 1.18 million real-world products and 12,087 crowd-sourced text instructions. Given a text instruction specifying a product requirement, an agent needs to navigate multiple types of webpages and issue diverse actions to find, customize, and purchase an item. WebShop provides several challenges for language grounding including understanding compositional instructions, query (re-)formulation, comprehending and acting on noisy text in webpages, and performing strategic exploration. We collect over 1,600 human demonstrations for the task, and 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 outperforms rule-based heuristics (9.6%) but is far lower than human expert 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 that agents trained on WebShop exhibit non-trivial sim-to-real transfer when evaluated on amazon.com, indicating the potential value of WebShop in developing practical web-based agents that can operate in the wild.

Live Demo

Try our sim-to-real agent in a live demo!

WebShop Environment

Try out the WebShop environment for yourself at the live site here!

The WebShop environment features a variety of states and a number of actions to transition from one state to the next. A state s represents one of four types of webpages:

At each state, an agent has two choices of actions: to either search a text query (e.g. search[Red shoes]) or choose a text button (e.g. choose[Size 9]). The following table lists the full set of available actions and the state transitions they correspond to.

Type Argument State → Next State
search [Query] Search → Results
choose Back to Search * → Search
choose Prev/Next Page Results → Results
choose [Product Title] Results → Item
choose [Option] Item → Item
choose Desc/Overview Item-Detail → Item
choose Buy Item → Episode End

Within this environment, an agent is given a human-provided text instruction and asked to purchase a product that matches the specifications. Rewards are automatically computed using a combination of programmatic matching functions that consider the attributes, type, options and price of the chosen product

In the WebShop environment setting, an agent is presented with a variety of challenges for language grounding, including understanding compositional instructions, query (re-)formulation, comprehending and acting on noisy text in webpages, and performing strategic exploration.


The below slides show the step-by-step actions of trajectories generated from different agents and entities performing the task of searching for a product based on a goal instruction.

Goal Instruction: I’m looking for a quick-release replacement fitness strap band; it should match my chic teal fitbit, and price lower than 40.00 dollars

These slides showcase, in order, the trajectories performed by 1) an MTurk worker 2) Rule Based Heuristic 3) Imitation Learning Agent, and 4) Imitation Learning + Reinforcement Learning Agent searching for a product on WebShop given the same goal instruction.

Sim-to-real Transfer: This last slideshow shows a trajectory generated by an Imitation Learning agent searching on the www.amazon.com website, achieved via sim-to-real transfer logic.

Follow Up Work

If interested, read about our follow up work where we improve upon and extend aspects of the WebShop environment. We identify WebShop’s promises of being realistic, faithful, and scalable as areas that can be fortified. We then make the following amendments for a V2 of WebShop:

  1. Make the reward function more faithful to human evaluation by redesigning the attribute and option scoring components.
  2. Survey 75 respondents for input regarding what features that are helpful to equivalent shopping tasks in the wild are missing from WebShop.
  3. Attempt to remove the need for crowd-sourcing product attributes and instructions by casting such problems as a summarization task using the product information.

To learn more, check out the paper, code, and poster that we are presenting at the Language and Reinforcement Learning workshop at NeurIPS 2022.


  bibtex_show = {true},
  title = {WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents},
  author = {Yao, Shunyu and Chen, Howard and Yang, John and Narasimhan, Karthik},
  booktitle = {ArXiv},
  year = {preprint},
  html = {https://arxiv.org/abs/2207.01206},
  tag = {NLP}


Shunyu Yao
Howard Chen
John Yang
Karthik Narasimhan