Jason Yecheng Ma*, Kausik Sivakumar*, Jason Yan, Osbert Bastani, Dinesh Jayaraman

University of Pennsylvania

This is the official repository of the L4DC 2023 paper TOM, a policy aware model learning method for Model-Based reinforcement learning. This repository also contains examples of running TOM as well as other baselines mentioned in the paper on standard Mujoco environments.

We recommend installing required packages over a virtual environment. This repo requires Python v3.7.

1. Install appropriate version of PyTorch
2. Download mjpro 150 binaries from https://www.roboti.us/download.html
3. Extract the file to ~/.mujoco/mjpro150
4. Install mujoco_py 1.50 from source https://github.com/openai/mujoco-py/releases/tag/1.50.1.0
5. Install gym - conda install -c conda-forge gym
6. conda install -c conda-forge tqdm
7. conda install -c conda-forge matplotlib=3.5.1

Run the following command with different values for the <env_name> and <mehod_name>

python mbpo.py --env <env_name> --method <method_name> --seed $SEED --use_disc  --f "chi"

<env_name> could be set to any of "Hopper-v2","Walker2d-v2","HalfCheetah-v2","Ant-v2","Humanoid-v2"
<method_name> could be set to any of "tom","mbpo","litm","vaml" to run the method TOM(ours), MBPO, PMAC, and VaGram respectively

We also support running TOM without a discriminator. This just presets a reward 1 for transitions from the current policy's rollouts and 0 to historical rollouts in the buffer. To run TOM without a discriminator, run

python mbpo.py --env <env_name> --method "tom" --seed $SEED --no_disc  --f "chi"

Methods MBPO, PMAC, and VaGram are the baselines against which we have evaluated our policy aware model learning method TOM in the paper.

If you have any questions regarding the code or paper, feel free to send all correspondences to [email protected] or [email protected].

This code has been partially adapted from MBPO; We thank the authors and contributors for open-sourcing their code.