Workflow-Guided Exploration (WGE) is a framework for exploring action sequences more efficiently when a small amount of demonstrations are available. It helps a reinforcement learning (RL) agent discover reward more quickly even when the reward is sparse.
Motivation and Setup
Our motivating task is learning an RL agent to use the Internet by controlling the web browser. Here is a simple example where the agent has to forward Bob’s email to Alice:
The input goal is different for each episode, and there can be multiple subtasks (e.g., forward an email, reply to an email, delete an email). The agent receives a sparse binary reward at the end of the episode.
To aid learning, suppose we also have access to a few (e.g., 10) human demonstrations of how to complete the tasks.
Instead of directly training a model on the demonstrations (which would lead to overfitting), we use the demonstrations to constrain exploration.
From each demonstration, we induce high-level workflows. A workflow (a path through the workflow lattice, like the one in bold) constrains the set of possible actions at each time step (e.g., the workflow step
Click(Tag("span"))only allows actions that click a
<span>element). We generate many workflows with different levels of restrictiveness.
When we perform exploration, we follow one of the workflows and sample actions that are within the constraints of the workflow. This way, the explored actions look similar to the demonstrated actions, so they are more likely to get positive reward.
But there are many possible workflows, many of which are too generic or too restrictive. We use a simple workflow policy to learn which workflows are good to use.
Evaluation is done on OpenAI Mini World of Bits (MiniWoB) benchmark. To aid further research, we have released the following resources:
Some results of the models learned using WGE, compared with models that use behavioral cloning + RL, are shown here:
|WGE (10 demos)||BC+RL (100 demos)||BC+RL (300 demos)||BC+RL (1000 demos)|
Evan Zheran Liu*, Kelvin Guu*, Panupong (Ice) Pasupat*, Tianlin Shi, Percy Liang. Reinforcement Learning on Web Interfaces using Workflow-Guided Exploration. ICLR 2018.