This repository is basically follow the Berkeley CS 294: Deep Reinforcement Learning. first assignment.
Dependencies: TensorFlow, MuJoCo version 1.31, OpenAI Gym
Note: MuJoCo versions until 1.5 do not support NVMe disks therefore won't be compatible with recent Mac machines. There is a request for OpenAI to support it that can be followed here.
In experts/
, the provided expert policies are:
- Ant-v1.pkl
- HalfCheetah-v1.pkl
- Hopper-v1.pkl
- Humanoid-v1.pkl
- Reacher-v1.pkl
- Walker2d-v1.pkl
The name of the pickle file corresponds to the name of the gym environment.
- download getid executable file, from MuJoco
- run the getid file
sudo chmod +x getid_linux
./getid_linux
- register and follow the instruction on email (download 131, put in ~/.mujoco/mjpro131, see link)
- create conda environment
conda create -n bc python=3.5 numpy scipy matplotlib theano keras ipython jupyter scikit-learn
source activate bc
- install mujoco-py 0.5.7, following the link
- install gym:
pip install gym==0.7.4
check bc_train.py
file to see how to define bc model and train it on expert data.
Example usage:
python bc_train.py Humanoid-v1 --render --train_steps 20000 --num_rollouts 20
check bc_eval.py
file to see how to evaluate trained model.
Example usage:
python bc_eval.py Humanoid-v1 --render --num_rollouts 20