Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916705606828032 |
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| author | Cherif, Lynn Kondrup, Flemming Venuto, David Anand, Ankit Precup, Doina Khetarpal, Khimya |
| author_facet | Cherif, Lynn Kondrup, Flemming Venuto, David Anand, Ankit Precup, Doina Khetarpal, Khimya |
| contents | Agents that can autonomously navigate the web through a graphical user interface (GUI) using a unified action space (e.g., mouse and keyboard actions) can require very large amounts of domain-specific expert demonstrations to achieve good performance. Low sample efficiency is often exacerbated in sparse-reward and large-action-space environments, such as a web GUI, where only a few actions are relevant in any given situation. In this work, we consider the low-data regime, with limited or no access to expert behavior. To enable sample-efficient learning, we explore the effect of constraining the action space through $\textit{intent-based affordances}$ -- i.e., considering in any situation only the subset of actions that achieve a desired outcome. We propose $\textbf{Code as Generative Affordances}$ $(\textbf{$\texttt{CoGA}$})$, a method that leverages pre-trained vision-language models (VLMs) to generate code that determines affordable actions through implicit intent-completion functions and using a fully-automated program generation and verification pipeline. These programs are then used in-the-loop of a reinforcement learning agent to return a set of affordances given a pixel observation. By greatly reducing the number of actions that an agent must consider, we demonstrate on a wide range of tasks in the MiniWob++ benchmark that: $\textbf{1)}$ $\texttt{CoGA}$ is orders of magnitude more sample efficient than its RL agent, $\textbf{2)}$ $\texttt{CoGA}$'s programs can generalize within a family of tasks, and $\textbf{3)}$ $\texttt{CoGA}$ performs better or on par compared with behavior cloning when a small number of expert demonstrations is available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_17282 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning Cherif, Lynn Kondrup, Flemming Venuto, David Anand, Ankit Precup, Doina Khetarpal, Khimya Artificial Intelligence Agents that can autonomously navigate the web through a graphical user interface (GUI) using a unified action space (e.g., mouse and keyboard actions) can require very large amounts of domain-specific expert demonstrations to achieve good performance. Low sample efficiency is often exacerbated in sparse-reward and large-action-space environments, such as a web GUI, where only a few actions are relevant in any given situation. In this work, we consider the low-data regime, with limited or no access to expert behavior. To enable sample-efficient learning, we explore the effect of constraining the action space through $\textit{intent-based affordances}$ -- i.e., considering in any situation only the subset of actions that achieve a desired outcome. We propose $\textbf{Code as Generative Affordances}$ $(\textbf{$\texttt{CoGA}$})$, a method that leverages pre-trained vision-language models (VLMs) to generate code that determines affordable actions through implicit intent-completion functions and using a fully-automated program generation and verification pipeline. These programs are then used in-the-loop of a reinforcement learning agent to return a set of affordances given a pixel observation. By greatly reducing the number of actions that an agent must consider, we demonstrate on a wide range of tasks in the MiniWob++ benchmark that: $\textbf{1)}$ $\texttt{CoGA}$ is orders of magnitude more sample efficient than its RL agent, $\textbf{2)}$ $\texttt{CoGA}$'s programs can generalize within a family of tasks, and $\textbf{3)}$ $\texttt{CoGA}$ performs better or on par compared with behavior cloning when a small number of expert demonstrations is available. |
| title | Cracking the Code of Action: a Generative Approach to Affordances for Reinforcement Learning |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2504.17282 |