+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| **Paper** | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks :cite:`finn2017modelagnostic` |
+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| **Framework(s)** | .. figure:: ./images/pytorch.png |
| | :scale: 10% |
| | :class: no-scaled-link |
| | |
| | PyTorch |
+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| **API Reference** | `garage.torch.algos.MAML <../_autoapi/garage/torch/algos/index.html#garage.torch.algos.maml>`_ |
+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| **Code** | `garage/torch/algos/maml.py <https://github.com/rlworkgroup/garage/blob/master/src/garage/torch/algos/maml.py>`_ |
+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| **Examples** | :ref:`maml_ppo_half_cheetah_dir`, :ref:`maml_trpo_half_cheetah_dir`, :ref:`maml_trpo_metaworld_ml1_push`, :ref:`maml_trpo_metaworld_ml10`. :ref:`maml_trpo_metaworld_ml45` |
+-------------------+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
MAML is a meta-learning algorithm that trains the parameters of a policy such that they generalize well to unseen tasks. In essence, this technique produces models that are good few shot learners and easy to fine-tune.
meta_batch_size=40,
inner_lr=0.1,
outer_lr=1e-3,
num_grad_updates=1,
meta_evaluator=None,
evaluate_every_n_epochs=1
.. figure:: ./images/pytorch.png
:scale: 10%
.. literalinclude:: ../../examples/torch/maml_ppo_half_cheetah_dir.py
.. figure:: ./images/pytorch.png
:scale: 10%
.. literalinclude:: ../../examples/torch/maml_trpo_half_cheetah_dir.py
.. figure:: ./images/pytorch.png
:scale: 10%
.. literalinclude:: ../../examples/torch/maml_trpo_metaworld_ml1_push.py
.. figure:: ./images/pytorch.png
:scale: 10%
.. literalinclude:: ../../examples/torch/maml_trpo_metaworld_ml10.py
.. figure:: ./images/pytorch.png
:scale: 10%
.. literalinclude:: ../../examples/torch/maml_trpo_metaworld_ml45.py
.. bibliography::
:style: unsrt
:filter: docname in docnames
This page was authored by Mishari Aliesa (@maliesa96).