Implementations of Multi-Task and Meta-Learning baselines for the Metaworld benchmark

0. Install uv
1. Create a virtual environment for the project: uv venv .venv --python 3.12
2. Activate the virtual environment: source .venv/bin/activate
3. Install the dependencies: uv pip install -e ".[cuda12]"

Here is how you can navigate this repository:

• examples contains code for running baselines.
• metaworld_algorithms/rl/algorithms contains the implementations of baseline algorithms (e.g. MTSAC, MTPPO, MAML, etc).
• metaworld_algorithms/nn contains the implementations of neural network architectures used in multi-task RL (e.g. Soft-Modules, PaCo, MOORE, etc).
• metaworld_algorithms/rl/networks.py contains code that wraps these neural network building blocks into agent components (actor networks, critic networks, etc).
• metaworld_algorithms/rl/buffers.py contains code for the buffers used.
• metaworld_algorithms/rl/algorithms/base.py contains code for training loops (e.g. on-policy, off-policy, meta-rl).
• meatworld_algorithms/envsmetaworld.py contains utilities for wrapping metaworld for use with these baselines.