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ARLA: Agent-based Reinforcement Learning Architecture

This repository contains the complete source code for the ARLA project, a multi-agent simulation platform designed for studying emergent behavior and complex cognitive architectures.

Quickstart (Clone → Run in ~60s)

This guide helps you set up the development environment and run a sample simulation in minutes.

1. Prerequisites

  • Python 3.11
  • Poetry: A modern dependency management tool for Python.
  • Make: For running helper commands from the Makefile.
  • Docker: For the containerized workflow.

2. Installation

Clone the repository and use poetry install to create a virtual environment and install all dependencies.

git clone git@github.com:renbytes/arla.git
cd arla
poetry install

This command handles everything: it creates a .venv, installs all dependencies, and links the local agent-* subpackages in editable mode.

3. Run a Simulation Locally

The main entrypoint for local simulations is the agent_sim.main module. You can run commands within the Poetry virtual environment by activating it first with poetry env activate, or by prefixing each command with poetry run.

# Activate the virtual environment (do this once per session)
poetry env activate
# Install the packages
poetry install
# Smoke test the local runner to see available options
poetry run arla --help
# Run an example simulation for 50 steps
poetry run arla --scenario simulations/soul_sim/scenarios/default.json --steps 50

4. Run with Docker Compose (Recommended)

The provided Makefile contains the simplest way to use the containerized environment.

Before running any of the following, you need to start up the docker container:

docker compose up -d

  1. Start Services: Build the Docker images and start the application, database, and other services in the background.

    make up

  2. Run Simulation: Execute the example simulation inside the running app container.

    make run-example

  3. View Logs: You can tail the logs from all running services using:

    make logs

  4. Stop Services: When you're finished, stop and remove all containers.

    make down

About

Framework for A/B testing multi-agent environments

renbytes.github.io/arla/

Resources

Readme

Contributing

Contributing
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GitHub - renbytes/arla: Framework for A/B testing multi-agent environments · GitHub
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ARLA: Agent-based Reinforcement Learning Architecture
This repository contains the complete source code for the ARLA project, a multi-agent simulation platform designed for studying emergent behavior and complex cognitive architectures.
Quickstart (Clone → Run in ~60s)
This guide helps you set up the development environment and run a sample simulation in minutes.
1. Prerequisites
  • Python 3.11
  • Poetry: A modern dependency management tool for Python.
  • Make: For running helper commands from the Makefile.
  • Docker: For the containerized workflow.
2. Installation
Clone the repository and use poetry install to create a virtual environment and install all dependencies.
git clone git@github.com:renbytes/arla.git
cd arla
poetry install
This command handles everything: it creates a .venv, installs all dependencies, and links the local agent-* subpackages in editable mode.
3. Run a Simulation Locally
The main entrypoint for local simulations is the agent_sim.main module. You can run commands within the Poetry virtual environment by activating it first with poetry env activate, or by prefixing each command with poetry run.
# Activate the virtual environment (do this once per session)
poetry env activate
# Install the packages
poetry install
# Smoke test the local runner to see available options
poetry run arla --help
# Run an example simulation for 50 steps
poetry run arla --scenario simulations/soul_sim/scenarios/default.json --steps 50
4. Run with Docker Compose (Recommended)
The provided Makefile contains the simplest way to use the containerized environment.
Before running any of the following, you need to start up the docker container:
docker compose up -d
  1. Start Services: Build the Docker images and start the application, database, and other services in the background.
    make up
  2. Run Simulation: Execute the example simulation inside the running app container.
    make run-example
  3. View Logs: You can tail the logs from all running services using:
    make logs
  4. Stop Services: When you're finished, stop and remove all containers.
    make down
About
Framework for A/B testing multi-agent environments
renbytes.github.io/arla/
Resources
Readme
Contributing
Contributing
Activity
Custom properties
Stars
1 star
Watchers
0 watching
Forks
0 forks
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, 'i');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - renbytes/arla: Framework for A/B testing multi-agent environments · GitHub
Skip to content
BranchesTags
Repository files navigation
ARLA: Agent-based Reinforcement Learning Architecture
This repository contains the complete source code for the ARLA project, a multi-agent simulation platform designed for studying emergent behavior and complex cognitive architectures.
Quickstart (Clone → Run in ~60s)
This guide helps you set up the development environment and run a sample simulation in minutes.
1. Prerequisites
  • Python 3.11
  • Poetry: A modern dependency management tool for Python.
  • Make: For running helper commands from the Makefile.
  • Docker: For the containerized workflow.
2. Installation
Clone the repository and use poetry install to create a virtual environment and install all dependencies.
git clone git@github.com:renbytes/arla.git
cd arla
poetry install
This command handles everything: it creates a .venv, installs all dependencies, and links the local agent-* subpackages in editable mode.
3. Run a Simulation Locally
The main entrypoint for local simulations is the agent_sim.main module. You can run commands within the Poetry virtual environment by activating it first with poetry env activate, or by prefixing each command with poetry run.
# Activate the virtual environment (do this once per session)
poetry env activate
# Install the packages
poetry install
# Smoke test the local runner to see available options
poetry run arla --help
# Run an example simulation for 50 steps
poetry run arla --scenario simulations/soul_sim/scenarios/default.json --steps 50
4. Run with Docker Compose (Recommended)
The provided Makefile contains the simplest way to use the containerized environment.
Before running any of the following, you need to start up the docker container:
docker compose up -d
  1. Start Services: Build the Docker images and start the application, database, and other services in the background.
    make up
  2. Run Simulation: Execute the example simulation inside the running app container.
    make run-example
  3. View Logs: You can tail the logs from all running services using:
    make logs
  4. Stop Services: When you're finished, stop and remove all containers.
    make down
About
Framework for A/B testing multi-agent environments
renbytes.github.io/arla/
Resources
Readme
Contributing
Contributing
Activity
Custom properties
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - renbytes/arla: Framework for A/B testing multi-agent environments · GitHub
Skip to content
BranchesTags
Repository files navigation
ARLA: Agent-based Reinforcement Learning Architecture
This repository contains the complete source code for the ARLA project, a multi-agent simulation platform designed for studying emergent behavior and complex cognitive architectures.
Quickstart (Clone → Run in ~60s)
This guide helps you set up the development environment and run a sample simulation in minutes.
1. Prerequisites
  • Python 3.11
  • Poetry: A modern dependency management tool for Python.
  • Make: For running helper commands from the Makefile.
  • Docker: For the containerized workflow.
2. Installation
Clone the repository and use poetry install to create a virtual environment and install all dependencies.
git clone git@github.com:renbytes/arla.git
cd arla
poetry install
This command handles everything: it creates a .venv, installs all dependencies, and links the local agent-* subpackages in editable mode.
3. Run a Simulation Locally
The main entrypoint for local simulations is the agent_sim.main module. You can run commands within the Poetry virtual environment by activating it first with poetry env activate, or by prefixing each command with poetry run.
# Activate the virtual environment (do this once per session)
poetry env activate
# Install the packages
poetry install
# Smoke test the local runner to see available options
poetry run arla --help
# Run an example simulation for 50 steps
poetry run arla --scenario simulations/soul_sim/scenarios/default.json --steps 50
4. Run with Docker Compose (Recommended)
The provided Makefile contains the simplest way to use the containerized environment.
Before running any of the following, you need to start up the docker container:
docker compose up -d
  1. Start Services: Build the Docker images and start the application, database, and other services in the background.
    make up
  2. Run Simulation: Execute the example simulation inside the running app container.
    make run-example
  3. View Logs: You can tail the logs from all running services using:
    make logs
  4. Stop Services: When you're finished, stop and remove all containers.
    make down
About
Framework for A/B testing multi-agent environments
renbytes.github.io/arla/
Resources
Readme
Contributing
Contributing
Activity
Custom properties
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - renbytes/arla: Framework for A/B testing multi-agent environments · GitHub
Skip to content
BranchesTags
Repository files navigation
ARLA: Agent-based Reinforcement Learning Architecture
This repository contains the complete source code for the ARLA project, a multi-agent simulation platform designed for studying emergent behavior and complex cognitive architectures.
Quickstart (Clone → Run in ~60s)
This guide helps you set up the development environment and run a sample simulation in minutes.
1. Prerequisites
  • Python 3.11
  • Poetry: A modern dependency management tool for Python.
  • Make: For running helper commands from the Makefile.
  • Docker: For the containerized workflow.
2. Installation
Clone the repository and use poetry install to create a virtual environment and install all dependencies.
git clone git@github.com:renbytes/arla.git
cd arla
poetry install
This command handles everything: it creates a .venv, installs all dependencies, and links the local agent-* subpackages in editable mode.
3. Run a Simulation Locally
The main entrypoint for local simulations is the agent_sim.main module. You can run commands within the Poetry virtual environment by activating it first with poetry env activate, or by prefixing each command with poetry run.
# Activate the virtual environment (do this once per session)
poetry env activate
# Install the packages
poetry install
# Smoke test the local runner to see available options
poetry run arla --help
# Run an example simulation for 50 steps
poetry run arla --scenario simulations/soul_sim/scenarios/default.json --steps 50
4. Run with Docker Compose (Recommended)
The provided Makefile contains the simplest way to use the containerized environment.
Before running any of the following, you need to start up the docker container:
docker compose up -d
  1. Start Services: Build the Docker images and start the application, database, and other services in the background.
    make up
  2. Run Simulation: Execute the example simulation inside the running app container.
    make run-example
  3. View Logs: You can tail the logs from all running services using:
    make logs
  4. Stop Services: When you're finished, stop and remove all containers.
    make down
About
Framework for A/B testing multi-agent environments
renbytes.github.io/arla/
Resources
Readme
Contributing
Contributing
Activity
Custom properties
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - renbytes/arla: Framework for A/B testing multi-agent environments · GitHub
Skip to content
BranchesTags
Repository files navigation
ARLA: Agent-based Reinforcement Learning Architecture
This repository contains the complete source code for the ARLA project, a multi-agent simulation platform designed for studying emergent behavior and complex cognitive architectures.
Quickstart (Clone → Run in ~60s)
This guide helps you set up the development environment and run a sample simulation in minutes.
1. Prerequisites
  • Python 3.11
  • Poetry: A modern dependency management tool for Python.
  • Make: For running helper commands from the Makefile.
  • Docker: For the containerized workflow.
2. Installation
Clone the repository and use poetry install to create a virtual environment and install all dependencies.
git clone git@github.com:renbytes/arla.git
cd arla
poetry install
This command handles everything: it creates a .venv, installs all dependencies, and links the local agent-* subpackages in editable mode.
3. Run a Simulation Locally
The main entrypoint for local simulations is the agent_sim.main module. You can run commands within the Poetry virtual environment by activating it first with poetry env activate, or by prefixing each command with poetry run.
# Activate the virtual environment (do this once per session)
poetry env activate
# Install the packages
poetry install
# Smoke test the local runner to see available options
poetry run arla --help
# Run an example simulation for 50 steps
poetry run arla --scenario simulations/soul_sim/scenarios/default.json --steps 50
4. Run with Docker Compose (Recommended)
The provided Makefile contains the simplest way to use the containerized environment.
Before running any of the following, you need to start up the docker container:
docker compose up -d
  1. Start Services: Build the Docker images and start the application, database, and other services in the background.
    make up
  2. Run Simulation: Execute the example simulation inside the running app container.
    make run-example
  3. View Logs: You can tail the logs from all running services using:
    make logs
  4. Stop Services: When you're finished, stop and remove all containers.
    make down
About
Framework for A/B testing multi-agent environments
renbytes.github.io/arla/
Resources
Readme
Contributing
Contributing
Activity
Custom properties
Stars
1 star
Watchers
0 watching
Forks
0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - renbytes/arla: Framework for A/B testing multi-agent environments · GitHub
Skip to content
BranchesTags
Repository files navigation
ARLA: Agent-based Reinforcement Learning Architecture
This repository contains the complete source code for the ARLA project, a multi-agent simulation platform designed for studying emergent behavior and complex cognitive architectures.
Quickstart (Clone → Run in ~60s)
This guide helps you set up the development environment and run a sample simulation in minutes.
1. Prerequisites
  • Python 3.11
  • Poetry: A modern dependency management tool for Python.
  • Make: For running helper commands from the Makefile.
  • Docker: For the containerized workflow.
2. Installation
Clone the repository and use poetry install to create a virtual environment and install all dependencies.
git clone git@github.com:renbytes/arla.git
cd arla
poetry install
This command handles everything: it creates a .venv, installs all dependencies, and links the local agent-* subpackages in editable mode.
3. Run a Simulation Locally
The main entrypoint for local simulations is the agent_sim.main module. You can run commands within the Poetry virtual environment by activating it first with poetry env activate, or by prefixing each command with poetry run.
# Activate the virtual environment (do this once per session)
poetry env activate
# Install the packages
poetry install
# Smoke test the local runner to see available options
poetry run arla --help
# Run an example simulation for 50 steps
poetry run arla --scenario simulations/soul_sim/scenarios/default.json --steps 50
4. Run with Docker Compose (Recommended)
The provided Makefile contains the simplest way to use the containerized environment.
Before running any of the following, you need to start up the docker container:
docker compose up -d
  1. Start Services: Build the Docker images and start the application, database, and other services in the background.
    make up
  2. Run Simulation: Execute the example simulation inside the running app container.
    make run-example
  3. View Logs: You can tail the logs from all running services using:
    make logs
  4. Stop Services: When you're finished, stop and remove all containers.
    make down
About
Framework for A/B testing multi-agent environments
renbytes.github.io/arla/
Resources
Readme
Contributing
Contributing
Activity
Custom properties
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Used by
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::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
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if (e.altKey && e.shiftKey && e.key === 'D') {
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GitHub - renbytes/arla: Framework for A/B testing multi-agent environments · GitHub
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ARLA: Agent-based Reinforcement Learning Architecture
This repository contains the complete source code for the ARLA project, a multi-agent simulation platform designed for studying emergent behavior and complex cognitive architectures.
Quickstart (Clone → Run in ~60s)
This guide helps you set up the development environment and run a sample simulation in minutes.
1. Prerequisites
  • Python 3.11
  • Poetry: A modern dependency management tool for Python.
  • Make: For running helper commands from the Makefile.
  • Docker: For the containerized workflow.
2. Installation
Clone the repository and use poetry install to create a virtual environment and install all dependencies.
git clone git@github.com:renbytes/arla.git
cd arla
poetry install
This command handles everything: it creates a .venv, installs all dependencies, and links the local agent-* subpackages in editable mode.
3. Run a Simulation Locally
The main entrypoint for local simulations is the agent_sim.main module. You can run commands within the Poetry virtual environment by activating it first with poetry env activate, or by prefixing each command with poetry run.
# Activate the virtual environment (do this once per session)
poetry env activate
# Install the packages
poetry install
# Smoke test the local runner to see available options
poetry run arla --help
# Run an example simulation for 50 steps
poetry run arla --scenario simulations/soul_sim/scenarios/default.json --steps 50
4. Run with Docker Compose (Recommended)
The provided Makefile contains the simplest way to use the containerized environment.
Before running any of the following, you need to start up the docker container:
docker compose up -d
  1. Start Services: Build the Docker images and start the application, database, and other services in the background.
    make up
  2. Run Simulation: Execute the example simulation inside the running app container.
    make run-example
  3. View Logs: You can tail the logs from all running services using:
    make logs
  4. Stop Services: When you're finished, stop and remove all containers.
    make down
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Framework for A/B testing multi-agent environments
renbytes.github.io/arla/
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