Sotopia: an Open-ended Social Learning Environment

Introduction

Sotopia is an open-ended social learning environment that allows agents to interact with each other and the environment. The environment is designed to be a platform for evaluating and faciliating social intelligence in language agents. The environment is designed to be open-ended, meaning that the environment can be easily extended to include new environments and new agents. The environment is also designed to be scalable, meaning that the environment can be easily scaled to include a large number of agents and environments.

Sotopia is accepted to ICLR 2024 as a spotlight ⭐!

@inproceedings{zhou2024sotopia,
  title = {SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents},
  author = {Zhou*, Xuhui and Zhu*, Hao and Mathur, Leena and Zhang, Ruohong and Qi, Zhengyang and Yu, Haofei and Morency, Louis-Philippe and Bisk, Yonatan and Fried, Daniel and Neubig, Graham and Sap, Maarten},
  journal = {ICLR},
  year = {2024},
  url = {https://openreview.net/forum?id=mM7VurbA4r},
}

Installation

This package supports Python 3.11 and above. In one line, pip install sotopia or pip install uv; uv pip install sotopia.

Or from scratch, use a virtual environment, e.g. with anaconda3: conda create -n sotopia python=3.11; conda activate sotopia; curl -sSL https://install.python-poetry.org | python3. Then, install the requirements and this package.

poetry install

OpenAI key is required to run the code. Please set the environment variable OPENAI_API_KEY to your key. The recommend way is to add the key to the conda environment:

conda env config vars set OPENAI_API_KEY=your_key

For some experiments, TogetherAI key is required to run the code. Please set the environment variable TOGETHER_API_KEY to your key. The recommend way is to add the key to the conda environment:

conda env config vars set TOGETHER_API_KEY=your_key

A redis-stack server is required to run the code. Please follow the instruction to start a redis-stack server or use an existing server. You can also check Q&A to initiate the redis server with the Sotopia data.

The REDIS_OM_URL need to be set before loading and saving agents:

conda env config vars set REDIS_OM_URL="redis://user:password@host:port"

Make a folder to store the logs:

mkdir logs

Easy Sample Server

You can view an episode demo with default parameters with the following:

import asyncio
from sotopia.samplers import UniformSampler
from sotopia.server import run_async_server

asyncio.run(
    run_async_server(
        model_dict={
            "env": "gpt-4",
            "agent1": "gpt-3.5-turbo",
            "agent2": "gpt-3.5-turbo",
        },
        sampler=UniformSampler(),
    )
)

or run

python examples/minimalist_demo.py

Contribution

Install dev options

Follow the installation instruction above and then, instead of running python -m pip install -e ., run the following commands:

python -m pip install -e ".[dev]"
mypy --install-types --non-interactive sotopia
python -m pip install pre-commit
pre-commit install

New branch for each feature

git checkout -b feature/feature-name and PR to main branch.

Before committing

Run pytest to make sure all tests pass (this will ensure dynamic typing passed with beartype) and mypy --strict . to check static typing. (You can also run pre-commit run --all-files to run all checks)

Check github action result

Check the github action result to make sure all tests pass. If not, fix the errors and push again.

Running Experiments

We use gin-config to configure the experiments. You don't need to be an expert to use it. The basic syntax is

python <code_file.py> --gin_file <gin_file1> --gin_file <gin_file2> '--gin.PARAM1=value1' '--gin.PARAM2=value2'

The --gin_file is used to load and compose the default configuration. The --gin.PARAM1=value1 is used to overwrite the default configuration. The later configuration will always overwrite the previous one.

Here is an example of running an experiment:

python examples/experiment_eval.py --gin_file sotopia_conf/generation_utils_conf/generate.gin --gin_file sotopia_conf/server_conf/server.gin --gin_file sotopia_conf/run_async_server_in_batch.gin '--gin.ENV_IDS=["01H7VFHPDZVVCDZR3AARA547CY"]' '--gin.AGENT1_MODEL="gpt-4"' '--gin.BATCH_SIZE=20' '--gin.PUSH_TO_DB=False' '--gin.TAG="test"'

For the complete set of parameters, please check the sotopia_conf folder.

To run a large batch of environments, you can change the ENV_IDS parameter in sotopia_conf/run_async_server_in_batch.gin to a list of environment ids. When gin.ENV_IDS==[], all environments on the DB will be used.

Getting access to your simulation

After running experiments, you can go to the examples/redis_stats.ipynb notebook to check the existing episodes (Episode Log section), as well as calculate the performance.

For the original Sotopia simulation in our paper's experiments, you can find how to get them in the Q&A section in the ./docs folder.

Adding new characters and environments

You can use the following function with the **kwargs being the properties of the AgentProfile class. This is the same for the scenarios/environments.

class AgentProfile(JsonModel):
    first_name: str = Field(index=True)
    last_name: str = Field(index=True)
    age: int = Field(index=True, default_factory=lambda: 0)
    occupation: str = Field(index=True, default_factory=lambda: "")
    gender: str = Field(index=True, default_factory=lambda: "")
    gender_pronoun: str = Field(index=True, default_factory=lambda: "")
    public_info: str = Field(index=True, default_factory=lambda: "")
    big_five: str = Field(index=True, default_factory=lambda: "")
    moral_values: list[str] = Field(index=False, default_factory=lambda: [])
    schwartz_personal_values: list[str] = Field(index=False, default_factory=lambda: [])
    personality_and_values: str = Field(index=True, default_factory=lambda: "")
    decision_making_style: str = Field(index=True, default_factory=lambda: "")
    secret: str = Field(default_factory=lambda: "")
    model_id: str = Field(default_factory=lambda: "")

class EnvironmentProfile(JsonModel):
    codename: str = Field(...)
    source: str = Field(...)
    scenario: str = Field(...)
    agent_goals: list[str] = Field(...)
    ...

from sotopia.database.persistent_profile import AgentProfile, EnvironmentProfile

def add_agent_to_database(**kwargs: dict[str, Any]) -> None:
    agent = AgentProfile(**kwargs)
    agent.save()

def add_env_profile(**kwargs: dict[str, Any]) -> None:
    env_profile = EnvironmentProfile(**kwargs)
    env_profile.save()