This repository contains implementations used for the DDP of Shafeef Omar (ED16B054).
The code is written in Python 3 and builds on Tensorflow. Many of the provided reinforcement learning environments require the Mujoco physics engine.
Ensure that you have a working MPI implementation (see here for more instructions).
For Ubuntu you can install MPI through the package manager:
sudo apt-get install libopenmpi-dev
pip install --upgrade virtualenv
virtualenv <venv-name>
source <venv-name>/bin/activate
If not done yet, install anaconda by following the instructions here.
Then reate a anaconda environment, activate it and install the requirements in requirements.txt.
conda create -n <env-name> python=3.6
source activate <env-name>
pip install -r requirements.txt
For running the majority of the provided Meta-RL environments, the Mujoco physics engine as well as a corresponding python wrapper are required. For setting up Mujoco and mujoco-py, please follow the instructions here.
To run the ProMP algorithm in a Mujoco environment with default configurations:
python run_scripts/pro-mp_run_mujoco.py
Additionally, in order to run the the gradient-based meta-learning methods MAML (Finn et. al., 2017) in a Mujoco environment with the default configuration execute, respectively:
python run_scripts/maml_run_mujoco.py
This repository includes environments introduced in (Duan et al., 2016, Finn et al., 2017), Rothfuss et al., 2018.