JAXL (JAX Learning)

Prerequisite:

• Python 3.9+
• MuJoCo (See here)
  • Remember to set LD_LIBRARY_PATH (e..g. export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<PATH_TO_MUJOCO>/.mujoco/mujoco210/bin)
  • For troubleshooting, see here

Installation:

You may simply pip install:

pip install -r requirements/all.txt
pip install -e .

When install Jax on GPU, note that we need to do the following instead (see here):

# CUDA 12 installation
# Note: wheels only available on linux.
pip install --upgrade "jax[cuda12_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

# CUDA 11 installation
# Note: wheels only available on linux.
pip install --upgrade "jax[cuda11_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/nvidia

If torch somehow conflicts, install the CPU-only version:

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

Potential memory issue:

export XLA_FLAGS=--xla_gpu_graph_level=0

Installing manipulator-learning Environments

See here

Conda on Salient

conda create --name jaxl python=3.11
pip install tensorflow-cpu tensorflow-datasets
conda install -c conda-forge glew
conda install -c conda-forge mesalib
conda install jaxlib=*=*cuda* jax cuda-nvcc -c conda-forge -c nvidia
conda install -c menpo glfw3
pip install patchelf
pip install -r requirements/conda.txt
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
pip install "cython<3"
pip install --upgrade "jax[cuda12_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
pip install -e .

Compute Canada

You may install this code on Compute Canada by simply running jaxl/installation/compute_canada/initial_setup.sh.

Experiments

Locally

To run a single experiment, you may execute:

python main.py --config_path=${config_path} --run_seed=${run_seed} --device=${device}

Examples of configuration file (i.e. config_path) are located under jaxl/configs. device can be cpu or gpu:<device_ids> (e.g. gpu:0,1).

To run multiple experiments, you may refer to scripts/mtil/local/inverted_pendulum. In particular, we first run generate_experts.py to construct a bash script, say run_all-inverted_pendulum.sh. Then, running said bash script will execute mutliple experiments.

Compute Canada

To run on Compute Canada, you may refer to scripts/mtil/compute_canada. In partciular, first run generate_expert_variants.py to construct dat file, consisting of different hyperparameters. Then, run sbatch generate_experts.sh to run each variant in paralllel.

Design Pattern

This codebase aims to combine both imitation learning and reinforcement learning into a single design pattern. To this end, we note that imitation learning consists of supervised learning, which is purely offline, whereas other approaches are more online. In particular, offline means that we are given a fixed batch of data, whereas online means that we receive a stream of data as the learner interacts with the environment. We emphasize that learner-environment interactions do not necessarily mean that the learner has influence over the distribution (e.g. online supervised learning).

Consequently, we have few main components:

• main.py is the main entrypoint to running experiments, where it defines the save paths, models, buffers, learners, and other necessary components for running an experiment.
• learning_utils.py orchestrates the learning progress. That is, it will instruct the learner to perform updates, keep track of learning progress, and checkpointing the models. It also provides utilities for constructing models, learners, and optimizers.
• buffers consists of various types of buffers. We currently support the standard replay buffer (TransitionNumPyBuffer) and trajectory-based replay buffer (TrajectoryNumPyBuffer). If we wish to add functionality such as prioritized experience replay and hindsight experience replay, we should consider using wrappers/decorators to extend the functionalities.
• learners consists of various types of learners. In particular, there are two types of learners: OfflineLearner and OnlineLearner, that are realizations of the abstract Learner class. By default, the Learner class enforces all learners have a way to perform checkpointing, initializing the parameters, and performing learning updates (if any).
  • OfflineLearner assumes that a batch of offline data is given, as a ReplayBuffer. The attribute is _buffer, which corresponds the "dataset" in the standard offline learning vocabulary. Alternatively, we can use torch.Dataset instead of more traditional offline learning approaches (e.g. MNIST, Omniglot, etc.). This can be specified through the configuration files using buffer_config for ReplayBuffer and dataset_config for torch.Dataset.
  • On the other hand, OnlineLearner assumes that it has access to a buffer, which is populated sample by sample. By varying the buffer size, we can obtain "streaming" online learners that assume to have no memory. The online learner can also interact with the environment, which is implemented via the gymnasium API.
• datasets contains offline datasets. In particular, we provide variations of different datasets including linear regression/classification, MNIST, and Omniglot. The corresponding wrapper.py allows us to augment the dataset (e.g. convert a standard supervised learning problem into an in-context learning problem).
• envs contains customized sequential decision making problems. We provide parameterized version of some gymnasium environments including MuJoCo, pendulum, and frozen lake, as well as parameterized version of Deepmind Control Suite.
• models implements commonly used machine learning model architectures. To account for special layers including recurrent networks and batch normalization, we construct a standardized interface for inference---a model is expected to output a tuple (model_output, carry, updates). model_output is the "prediction" of the model, carry is often known as the hidden state, and updates is used for storing information for layers such as batch normalization.

Supported Custom Environments

We provide customized MuJoCo environments (in both Gymnasium and Deepmind Control Suite) that randomize the physical parameters at the initialization of the environment. This allows us to do some form of multi-task learning, or some form of domain randomization. We achieve this by modifying the XML before passing it to the MuJoCo environment wrapper provided by MujocoEnv or control.Environment. For more details, the environments are located under envs/mujoco and envs/dmc.

Styling

• We use black to format code and pyment to generate docstrings with reST style.

Generating Docstrings

To generate the docstrings in HTML format, we use sphinx.

cd docs
sphinx-apidoc -f -o . ..
make html​

Long-term Modification

• JAX implementation of the replay buffers
• Revisit A2C, PPO, and MTBC for better loss construction. It seems to be using a different design pattern.
• Revisit how we construct models and optimizers. There should be a more elegant way.