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mbrl-jax

A library intended for running model-based RL experiments, written with JAX. Currently only includes a reimplementation of PETS. Other algorithm implementations are planned soon!

Warning: This is a work-in-progress, and has not been evaluated on harder environments! Please let me know if you find any bugs.

TODOs:

  1. Evaluate on harder environments (e.g. HalfCheetah, Ant).
  2. Implement other more recent model-based algorithms.

Installing Dependencies

A Dockerfile with all required dependencies is provided in the /docker/ folder, together with an accompanying docker-compose.yml file. Remember to include the appropriate mounts in the docker-compose file as necessary for your needs!

Running Experiments

A starter script for running an example experiment on cartpole is provided in model_based_experiment.py. This script can be run via

  python3 model_based_experiment.py
      --logdir                   DIR      (optional)    Directory for saving checkpoints and 
                                                        rollout recordings. 
      --save-every               FREQ     (optional)    Saving frequency. Defaults to 1 (i.e. 
                                                        save after every iteration)
      --keep-all-checkpoints              (optional)    Flag which enables saving of all 
                                                        checkpoints (instead of only the most 
                                                        recent one).
      --iters                    ITERS    (optional)    Number of training iterations to run.
                                                        Defaults to 100.
      -s                         SEED     (optional)    Experiment random seed. If not 
                                                        provided, uniformly chosen in 
                                                        [0, 10000).
      env                        ENV      (required)    Experiment environment. Currently 
                                                        supports [`MujocoCartpole-v0`,
                                                        `HalfCheetah-v3`]
      agent_type                 AGENT    (required)    Agent type. Choices: [PETS, Policy].

For example, to run PETS and save recordings of rollouts to /external/:

python3 model_based_experiment.py --logdir /external/ MujocoCartpole-v0 PETS