This is a clone of OpenAI's Spinning Up in PyTorch. Spinning Up is an awesome educational resource produced by Josh Achiam, a research scientist at OpenAI, that makes it easier to learn about deep reinforcement learning (deep RL).
Fired Up requires Python3, PyTorch, OpenAI Gym, and OpenMPI.
Fired Up is currently only supported on Linux and OSX. It may be possible to install on Windows, though I haven't tested this OS.
We recommend installing Python through Anaconda. Anaconda is a Python distribution that includes many useful packages especially for scientific computing, as well as an environment manager called conda that makes package management simple.
Download and install Anaconda 2018.x (at time of writing, 2018.12) Python 3.7. Then create a conda environment for organizing packages used in Fired Up:
conda create -n firedup python=3.7
To use Python from the environment you just created, activate the environment with:
source activate firedup
You can alternatively use virtualenv with the Python3 version you have. Just install it via pip3 and then:
virtualenv firedup
To activate this virtual environment you need to:
source /path/to/firedup/bin/activate
sudo apt update && sudo apt install libopenmpi-dev
Installation of system packages on Mac requires Homebrew. With Homebrew installed, run the following:
brew install openmpi
git clone https://github.com/kashif/firedup.git
cd firedup
pip install -e .
Fired Up defaults to installing everything in Gym except the MuJoCo environments.
To see if you've successfully installed Fired Up, try running PPO in the LunarLander-v2 environment with:
python -m fireup.run ppo --hid "[32,32]" --env LunarLander-v2 --exp_name installtest --gamma 0.999
After it finishes training, watch a video of the trained policy with:
python -m fireup.run test_policy data/installtest/installtest_s0
And plot the results with:
python -m fireup.run plot data/installtest/installtest_s0
The following algorithms are implemented in the Fired Up package:
- Vanilla Policy Gradient (VPG)
- Trust Region Policy Optimization (TRPO)
- Proximal Policy Optimization (PPO)
- Deep Q-Network (DQN)
- Deep Deterministic Policy Gradient (DDPG)
- Twin Delayed DDPG (TD3)
- Soft Actor-Critic (SAC)
They are all implemented with MLP (non-recurrent) actor-critics, making them suitable for fully-observed, non-image-based RL environments, e.g. the Gym Mujoco environments.