Imitation Learning

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.

Set up on ubuntu

• 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

Train

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

Evaluation

check bc_eval.py file to see how to evaluate trained model.
Example usage:

• python bc_eval.py Humanoid-v1 --render --num_rollouts 20