ai-traineree

The intention is to have a zoo of Deep Reinforcment Learning methods and showcasing their application on some environments.

Read more in the doc: ReadTheDocs AI-Traineree.

Why another?

The main reason is the implemention philosophy. We strongly believe that agents should be emerged in the environment and not the other way round. Majority of the popular implementations pass environment instance to the agent as if the agent was the focus point. This might ease implementation of some algorithms but it isn't representative of the world; agents want to control the environment but that doesn't mean they can/should.

That, and using PyTorch instead of Tensorflow or JAX.

Quick start

To get started with training your RL agent you need three things: an agent, an environment and a runner. Let's say you want to train a DQN agent on OpenAI CartPole-v1: ```python from aitraineree.agents.dqn import DQNAgent from aitraineree.runners.env_runner import EnvRunner from aitraineree.tasks import GymTask

task = GymTask('CartPole-v1') agent = DQNAgent(task.obsspace, task.actionspace) env_runner = EnvRunner(task, agent)

scores = env_runner.run() ```

or execute one of provided examples

\$ python -m examples.cart_dqn

That's it.

Installation

PyPi (recommended)

The quickest way to install package is through pip.

\$ pip install ai-traineree

Git repository clone

As usual with Python, the expectation is to have own virtual environment and then pip install requirements. For example, ```bash

python -m venv .venv git clone git@github.com:laszukdawid/ai-traineree.git source .venv/bin/activate python setup.py install ```

Current state

Playing gym

One way to improve learning speed is to simply show them how to play or, more researchy/creepy, provide a proper seed. This isn't a general rule, since some algorithms train better without any human interaction, but since you're on GitHub... that's unlikely your case. Currently there's a script interact.py which uses OpenAI Gym's play API to record moves and AI Traineree to store them in a buffer. Such buffers can be loaded by agents on initiation.

This is just a beginning and there will be more work on these interactions.

Requirement: Install pygame.

Agents

| Short | Progress | Link | Full name | Doc | | ------- | --------------------------------------------- | -------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------- | ------------------------------------------------------------------------ | | DQN | Implemented | DeepMind | Deep Q-learning Network | Doc | | DDPG | Implemented | arXiv | Deep Deterministic Policy Gradient | Doc | | D4PG | Implemented | arXiv | Distributed Distributional Deterministic Policy Gradients | Doc | | TD3 | Implemented | arXiv | Twine Delayed Deep Deterministic policy gradient | Doc | | PPO | Implemented | arXiv | Proximal Policy Optimization | Doc | | SAC | Implemented | arXiv | Soft Actor Critic | Doc | | TRPO | | arXiv | Trust Region Policy Optimization | | RAINBOW | Implemented | arXiv | DQN with a few improvements | Doc |

Multi agents

We provide both Multi Agents agents entities and means to execute them against supported (below) environements. However, that doesn't mean one can be used without the other.

| Short | Progress | Link | Full name | Doc | | ------ | ------------------------------------------------- | ----------------------------------------- | ---------------------- | ---------------------------------------------------------------------------- | | IQL | Implemented | | Independent Q-Learners | Doc | | MADDPG | Implemented | arXiv | Multi agent DDPG | Doc |

Loggers

Supports using Tensorboard (via PyTorch's SummaryWriter) and Neptune to display metrics. Wrappers are provided as TensorboardLogger and NeptuneLogger.

Note: In order to use Neptune one needs to install neptune-client (pip install neptune-client).

Environments

| Name | Progress | Link | | -------------------- | ------------------ | -------------------------------------------------------------------------------------------- | | OpenAI Gym - Classic | Done | | OpenAI Gym - Atari | Done | | OpenAI Gym - MuJoCo | Not interested. | | PettingZoo | Initial support | Page / GitHub | | Unity ML | Somehow supported. | Page | | MAME Linux emulator | Interested. | Official page |

Development

We are open to any contributions. If you want to contribute but don't know what then feel free to reach out (see Contact below). The best way to start is through updating documentation and adding tutorials. In addition there are many other things that we know of which need improvement but also plenty that we don't know of.

Setting up development environment requires installing dev and test extra packages. The dev extras are for mainly for linting and formatting, and the test is for running tests. We recommend using pip so to install everything requires for development run

bash $ pip install -e .[dev,test]

Once installed, please configure your IDE to use black as formatter, pycodestyle as linter, and isort for sorting imports. All these are included in the dev extra packages.

Contact

Should we focus on something specificallly? Let us know by opening a feature request GitHub issue or contacting through ai-traineree@dawid.lasz.uk.

Citing project

latex @misc{ai-traineree, author = {Laszuk, Dawid}, title = {AI Traineree: Reinforcement learning toolset}, year = {2020}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/laszukdawid/ai-traineree}}, }