Implementations of Multi-Task and Meta-Learning baselines for the Metaworld benchmark
- Install uv
- Create a virtual environment for the project:
uv venv .venv --python 3.12 - Activate the virtual environment:
source .venv/bin/activate - Install the dependencies:
uv pip install -e ".[cuda12]"
Note
To use other accelerators, replace cuda12 with the appropriate accelerator name.
Valid options are cpu, tpu, cuda12, and metal.
Here is how you can navigate this repository:
examplescontains code for running baselines.metaworld_algorithms/rl/algorithmscontains the implementations of baseline algorithms (e.g. MTSAC, MTPPO, MAML, etc).metaworld_algorithms/nncontains the implementations of neural network architectures used in multi-task RL (e.g. Soft-Modules, PaCo, MOORE, etc).metaworld_algorithms/rl/networks.pycontains code that wraps these neural network building blocks into agent components (actor networks, critic networks, etc).metaworld_algorithms/rl/buffers.pycontains code for the buffers used.metaworld_algorithms/rl/algorithms/base.pycontains code for training loops (e.g. on-policy, off-policy, meta-rl).meatworld_algorithms/envsmetaworld.pycontains utilities for wrapping metaworld for use with these baselines.