This is an example implementation of the paper "Cross Domain Robot Imitation with Invariant Representation".

The experiments are dependent on gym, mujoco-py, and torch. Make sure you have installed them properly.

The example folder contains a quick demo on the InvertedDoublePendulum environment.

To train a CDIL agent with pretrained representation network (in extrapolation mode), run the following command:

python ./example/idp.py --cuda --c1 1.3 --c2 1.5 --c3 1.4 --rollout_length 5000 --eval_interval 5000 --num_steps 5000000 --buffer ./assets/idp_expert_buffer.pth --embedding ./assets/idp_pretrained.pth --seed 0

In the above command, --c1 1.3 --c2 1.5 --c3 1.4 specifies the environment parameter (1.3, 1.5, 1.4). The parameters of experts are around (0.9, 0.9, 0.9). Note that these parameters are absolute values (rather than relative value in the paper). You can change these parameters to do interpolation as well.

--buffer ./assets/idp_expert_buffer.pth specifies the expert demonstration.

--embedding ./assets/idp_pretrained.pth specifies the pretrained representation network.

The return for this example will converge after 600k steps.

The random and expert experience used to train the representation network are in the ./assets/idp_expert_random_buffer.pkl.

If you want to train the representation network by yourself, run the following command:

python ./example/idp_train_representation.py

It will create a representation_logs folder, in which you can find the latest model as training goes on.

You can then use the trained model for imitation learning.

Navigate to the ./example/idp_train_representation.py, and disable the dynamics loss by changing the c_f value to 0.0. Then, train the representation model and the agent again.

This time, you will find the performance of agent degrades, in particular in the extrapolation experiment.

We gratefully thank ku2482 for a neat imitation learning framework. 🙂