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Reinforcement learning framework based on Pytorch for implementing custom deep learning models on custom environments using state of the art and modular reinforcement learning algorithms.

Developing deep learning models is hard enough to also be worrying about implementing them correctly in the last reinforcement learning algorithm you saw in a paper. Lazaro takes that weight out of the shoulders of the people who work with reinforcement learning, and let developers focus on their custom deep learning models that solve their custom environments.

Installation

Lazaro package is available on PyPI for Python versions greater or equal to 3.7.

pip install lazaro

Usage

Import torch and lazaro packages

import torch
import torch.nn.functional as F
import lazaro as lz

Choose an environment to train your model. You can create one yourself or choose one from the environments repository. Here is a cool tutorial that shows how to build an environment yourself from scratch.

env = lz.environments.CartPole()
OBSERVATION_SPACE = 4  # position, speed, angle, rotation speed 
ACTION_SPACE = 2  # move right, move left

Create your custom model using PyTorch.

class CustomModel(torch.nn.Module):
    def __init__(self):
        super(CustomModel, self).__init__()
        # your last layer can have an arbitrary number of output neurons, 
        # lazaro will handle the rest of the model for you
        self.linear = torch.nn.Linear(OBSERVATION_SPACE, 128) 

    def forward(self, x):
        return F.relu(self.linear(x))

Create your agent. You can select any agent and mix it with any exploration policy and any replay buffer algorithm.

# mix any agent with any exploration policy and any replay buffer
class CustomAgent(lz.agents.explorers.NoisyExplorer,
                  lz.agents.replay_buffers.NStepsPrioritizedReplayBuffer,
                  lz.agents.DoubleDuelingDqnAgent):  
    
    # implement the model factory. It will be used to embed your torch model into the agent
    def model_factory(self):
        return CustomModel()
    
    # lazaro expects to receive data as torch.Tensor
    def preprocess(self, x):  # x is the raw state that the environment returns
        return torch.from_numpy(x.astype("float32"))

Instantiate your agent and train it in your environment.

agent = CustomAgent(action_space=ACTION_SPACE)
agent.train(env)

Custom snake game environment

If you want to know how to create your own environment, checkout this tutorial, where you will build the snake game from scratch in a few lines of code and train an agent on it

Tensorboard integration

You can visualize the learning progress and the agent's neural network graph using Tensorboard. It's very simple:

class CustomAgent(lz.agents.explorers.NoisyExplorer,
                  lz.agents.replay_buffers.NStepsPrioritizedReplayBuffer,
                  lz.agents.loggers.TensorBoardLogger,  # <- Just add this, and voila!
                  lz.agents.DoubleDuelingDqnAgent):  
    
    def model_factory(self):
        return CustomModel()
    
    def preprocess(self, x):
        return torch.from_numpy(x.astype("float32"))

Once the training has started, you can go to the Tensorboard local uri (http://localhost:6006) and see useful info like this: