Welcome to the world of AgentNet, a lightweight library designed to empower you to build and train deep reinforcement learning and custom recurrent networks effortlessly! With AgentNet, you can teach your neural networks to play games and tackle complex Markov Decision Processes—all while enjoying an intuitive framework that supports the Lasagne deep learning library.

What is AgentNet?

Imagine AgentNet as your very own personal trainer for neural networks, guiding them through rigorous training sessions to master challenging games like OpenAI Gym. This framework is structured to work seamlessly with Lasagne, providing all the delicious ingredients like convolutions, poolings, and dropouts—essentially everything a neural network needs to thrive.

Installation

Getting AgentNet up and running is straightforward! Here’s how to do it:

Quick Installation Steps:

• Install the bleeding-edge version of Lasagne: Lasagne Installation.
• Run the following command:

  [sudo] pip install --upgrade https://github.com/yandexdataschool/AgentNet/archive/master.zip

Full Installation (with Examples):

1. Clone the repository:

   git clone https://github.com/yandexdataschool/AgentNet.git 
   cd AgentNet

2. Install dependencies:

   pip install -r requirements.txt

3. Install the library itself:

   pip install -e .

Using Docker

If you’re working with Docker, here’s how you can set it up:

1. For Windows and Mac, install Docker Kitematic and launch the justheuristicagentnet container. Then click on the web preview.
2. For Linux/Unix systems:
   1. Install Docker: Docker Installation.
   2. Ensure the Docker daemon is running:

      sudo service docker start

   3. Make sure no application is using port 1234 (this is the default port that can be changed).
   4. Run the Docker container:

      [sudo] docker run -d -p 1234:8888 justheuristicagentnet

   5. Access it via your browser at: localhost:1234.

Documentation and Tutorials

Dive into the world of AgentNet by exploring the documentation and available tutorials:

• Use Binder for a quick demo.
• Refer to classwork.ipynb and classwork_solution.ipynb for in-depth tutorials and solutions.
• Documentation pages can be found here.

Code Example: Playing Atari SpaceInvaders

Let’s illustrate how to set up a basic interaction using AgentNet with an analogy. Picture your neural network as a young gamer trying to master the game Space Invaders. Through trials and errors—much like practicing to improve its performance—our gamer learns the game dynamics over time. In concrete terms, AgentNet allows for the implementation of a setup to train your neural network to play the game using the Convolutional NN.

Your gamer (agent) will learn from rewards and penalties as they navigate through obstacles (game levels), allowing the agent to gradually improve its strategy. This level of training is facilitated through several reinforcement learning techniques that AgentNet supports, such as:

• Q-learning
• N-step Q-learning
• SARSA
• N-step Advantage Actor-Critic (A2C)
• N-step Deterministic Policy Gradient (DPG)

So, every time your gamer defeats a level, they get better and better. AgentNet makes sure that your “gamer” keeps learning under the hood!

Troubleshooting

If you run into issues while using AgentNet, here are a few troubleshooting ideas:

• Make sure you have all dependencies installed correctly. Check the detailed installation guide.
• If you are using Docker, ensure that the Docker daemon is running, and check if the default port is available.
• Refer to the documentation for any unresolved queries.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Enjoy your journey of building and training your deep learning models with AgentNet!