virtualhome-eval 0.1.0

Embodied agent interface evaluation for VirtualHome

Installation and Usage Guide for virtualhome-eval

Install dependencies

pip install virtualhome_eval

Usage

To run virtualhome_eval, use the following commands with arguments

from virtualhome_eval.agent_eval import agent_evaluation
agent_evaluation(mode=[generate_prompts, evaluate_results], eval_type=[goal_interpretation, action_sequence, transition_modeling], llm_response_path=[YOUR LLM OUTPUT DIR])

Parameters

• mode: Specifies either generate prompts or evaluate results. Options are:
  • generate_prompts
  • evaluate_results
• eval_type: Specifies the evaluation task type. Options are:
  • goal_interpretation
  • action_sequence
  • subgoal_decomposition
  • transition_model
• llm_response_path: The path of LLM output directory to be evaluated. It is "" by default, using the existing outputs at directory virtualhome_eval/llm_response/. The function will evaluate all LLM outputs under the directory.
• dataset: The dataset type. Options:
  • virtualhome
  • behavior
• output_dir: The directory to store the output results. By default, it is at output/ of current path.

Example usage

1. To generate prompts for goal_interpretation:

agent_evaluation(mode='generate_prompts',  eval_type='goal_interpretation')

2. To evaluate LLM outputs for goal_interpretation:

results = agent_evaluation(mode='evaluate_results', eval_type='goal_interpretation')

3. To generate prompts for action_sequence:

agent_evaluation(mode='generate_prompts',  eval_type='action_sequence')

4. To evaluate LLM outputs for action_sequence:

results = agent_evaluation(mode='evaluate_results', eval_type='action_sequence')

5. To generate Virtualhome prompts for transition_model:

agent_evaluation(mode='generate_prompts',  eval_type='transition_model')

6. To evaluate LLM outputs on Virtualhome for transition_model:

results = agent_evaluation(mode='evaluate_results', eval_type='transition_model')

7. To generate prompts for subgoal_decomposition:

agent_evaluation(mode='generate_prompts',  eval_type='subgoal_decomposition')

8. To evaluate LLM outputs for subgoal_decomposition:

results = agent_evaluation(mode='evaluate_results', eval_type='subgoal_decomposition')