NousResearch/local_generative_agents

NousResearch/local_generative_agents

Description: Generative Agents: Interactive Simulacra of Human Behavior - with Local LLMs

License: Apache-2.0

Stars: 27

Forks: 10

Open issues: 0

Created: 2023-08-13T18:54:11Z

Pushed: 2023-08-15T07:01:10Z

Default branch: main

Fork: yes

Parent repository: joonspk-research/generative_agents

Archived: no

README:

Generative Agents: Interactive Simulacra of Human Behavior

This repository accompanies our research paper titled "Generative Agents: Interactive Simulacra of Human Behavior." It contains our core simulation module for generative agents—computational agents that simulate believable human behaviors—and their game environment. Below, we document the steps for setting up the simulation environment on your local machine and for replaying the simulation as a demo animation.

Setting Up the Environment

To set up your environment, you will need to generate a utils.py file that contains your OpenAI API key and download the necessary packages.

Step 1. Generate Utils File

In the reverie/backend_server folder (where reverie.py is located), create a new file titled utils.py and copy and paste the content below into the file:

# Copy and paste your OpenAI API Key
openai_api_key = ""
# Put your name
key_owner = ""

maze_assets_loc = "../../environment/frontend_server/static_dirs/assets"
env_matrix = f"{maze_assets_loc}/the_ville/matrix"
env_visuals = f"{maze_assets_loc}/the_ville/visuals"

fs_storage = "../../environment/frontend_server/storage"
fs_temp_storage = "../../environment/frontend_server/temp_storage"

collision_block_id = "32125"

# Verbose
debug = True

Replace ` with your OpenAI API key, and ` with your name.

Step 2. Install requirements.txt

Install everything listed in the requirements.txt file (I strongly recommend first setting up a virtualenv as usual). A note on Python version: we tested our environment on Python 3.9.12.

Running a Simulation

To run a new simulation, you will need to concurrently start two servers: the environment server and the agent simulation server.

Step 1. Starting the Environment Server

Again, the environment is implemented as a Django project, and as such, you will need to start the Django server. To do this, first navigate to environment/frontend_server (this is where manage.py is located) in your command line. Then run the following command:

python manage.py runserver

Then, on your favorite browser, go to http://localhost:8000/. If you see a message that says, "Your environment server is up and running," your server is running properly. Ensure that the environment server continues to run while you are running the simulation, so keep this command-line tab open! (Note: I recommend using either Chrome or Safari. Firefox might produce some frontend glitches, although it should not interfere with the actual simulation.)

Step 2. Starting the Simulation Server

Open up another command line (the one you used in Step 1 should still be running the environment server, so leave that as it is). Navigate to reverie/backend_server and run reverie.py.

python reverie.py This will start the simulation server. A command-line prompt will appear, asking the following: "Enter the name of the forked simulation: ". To start a 3-agent simulation with Isabella Rodriguez, Maria Lopez, and Klaus Mueller, type the following:

base_the_ville_isabella_maria_klaus The prompt will then ask, "Enter the name of the new simulation: ". Type any name to denote your current simulation (e.g., just "test-simulation" will do for now).

test-simulation Keep the simulator server running. At this stage, it will display the following prompt: "Enter option: "

Step 3. Running and Saving the Simulation

On your browser, navigate to http://localhost:8000/simulator_home. You should see the map of Smallville, along with a list of active agents on the map. You can move around the map using your keyboard arrows. Please keep this tab open. To run the simulation, type the following command in your simulation server in response to the prompt, "Enter option":

run Note that you will want to replace ` above with an integer indicating the number of game steps you want to simulate. For instance, if you want to simulate 100 game steps, you should input run 100`. One game step represents 10 seconds in the game.

Your simulation should be running, and you will see the agents moving on the map in your browser. Once the simulation finishes running, the "Enter option" prompt will re-appear. At this point, you can simulate more steps by re-entering the run command with your desired game steps, exit the simulation without saving by typing exit, or save and exit by typing fin.

The saved simulation can be accessed the next time you run the simulation server by providing the name of your simulation as the forked simulation. This will allow you to restart your simulation from the point where you left off.

Step 4. Replaying a Simulation

You can replay a simulation that you have already run simply by having your environment server running and navigating to the following address in your browser: http://localhost:8000/replay//. Please make sure to replace ` with the name of the simulation you want to replay, and ` with the integer time-step from which you wish to start the replay.

For instance, by visiting the following link, you will initiate a pre-simulated example, starting at time-step 1: http://localhost:8000/replay/July1_the_ville_isabella_maria_klaus-step-3-20/1/

Step 5. Demoing a Simulation

You may have noticed that all character sprites in the replay look identical. We would like to clarify that the replay function is primarily intended for debugging purposes and does not prioritize optimizing the size of the simulation folder or the visuals. To properly demonstrate a simulation with appropriate character sprites, you will need to compress the simulation first. To do this, open the compress_sim_storage.py file located in the reverie directory using a text editor. Then, execute the compress function with the name of the target simulation as its input. By doing so, the simulation file will be compressed, making it ready for demonstration.

To start the demo, go to the following address on your…