feat(examples): TorchRL - MAML integration

CHANGELOG.md

@@ -13,6 +13,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
+- Add MAML example with TorchRL integration by [@vmoens](https://github.com/vmoens) and [@Benjamin-eecs](https://github.com/Benjamin-eecs) in [#12](https://github.com/metaopt/TorchOpt/pull/12).
 - Add optional argument `params` to update function in gradient transformations by [@XuehaiPan](https://github.com/XuehaiPan) in [#65](https://github.com/metaopt/torchopt/pull/65).
 - Add option `weight_decay` option to optimizers by [@XuehaiPan](https://github.com/XuehaiPan) in [#65](https://github.com/metaopt/torchopt/pull/65).
 - Add option `maximize` option to optimizers by [@XuehaiPan](https://github.com/XuehaiPan) in [#64](https://github.com/metaopt/torchopt/pull/64).

docs/source/developer/contributor.rst

@@ -4,3 +4,4 @@ Contributor
 We always welcome contributions to help make TorchOpt better. Below is an incomplete list of our contributors (find more on `this page <https://github.com/metaopt/torchopt/graphs/contributors>`_).
 
 * Yao Fu (`future-xy <https://github.com/future-xy>`_)
+* Vincent Moens (`vmoens <https://github.com/vmoens>`_)

examples/MAML-RL/README.md

@@ -9,6 +9,8 @@ Specify the seed to train.
 ```bash
 ### Run MAML
 python maml.py --seed 1
+### Run torchrl MAML implementation
+python maml_torchrl.py --seed 1
 ```
 
 ## Results

examples/MAML-RL/helpers/policy.py

@@ -16,7 +16,6 @@
 # https://github.com/tristandeleu/pytorch-maml-rl
 # ==============================================================================
 
-import torch
 import torch.nn as nn
 from torch.distributions import Categorical
 
@@ -27,12 +26,8 @@ class CategoricalMLPPolicy(nn.Module):
     with discrete action spaces (eg. `TabularMDPEnv`).
     """
 
-    def __init__(
-        self,
-        input_size,
-        output_size,
-    ):
-        super(CategoricalMLPPolicy, self).__init__()
+    def __init__(self, input_size, output_size):
+        super().__init__()
         self.torso = nn.Sequential(
             nn.Linear(input_size, 32),
             nn.ReLU(),

examples/MAML-RL/helpers/policy_torchrl.py

@@ -0,0 +1,88 @@
+# Copyright 2022 MetaOPT Team. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import torch
+import torch.nn as nn
+from torch.distributions import Categorical
+from torchrl.modules import (
+    ActorValueOperator,
+    OneHotCategorical,
+    ProbabilisticActor,
+    TensorDictModule,
+    ValueOperator,
+)
+
+
+class Backbone(nn.Module):
+    def __init__(self, input_size, output_size):
+        super().__init__()
+        self.torso = nn.Sequential(
+            nn.Linear(input_size, 32),
+            nn.ReLU(),
+            nn.Linear(32, 32),
+            nn.ReLU(),
+        )
+
+    def forward(self, inputs, params=None):
+        embedding = self.torso(inputs)
+        return embedding
+
+
+class CategoricalSubNet(nn.Module):
+    def __init__(self, input_size, output_size):
+        super().__init__()
+        self.policy_head = nn.Linear(32, output_size)
+
+    def forward(self, embedding, params=None):
+        logits = self.policy_head(embedding)
+        return logits
+
+
+class ValueSubNet(nn.Module):
+    def __init__(self, input_size, output_size):
+        super().__init__()
+        self.value_head = nn.Linear(32, 1)
+
+    def forward(self, embedding, params=None):
+        value = self.value_head(embedding)
+        return value
+
+
+class ActorCritic(ActorValueOperator):
+    def __init__(self, input_size, output_size):
+        super().__init__(
+            TensorDictModule(
+                spec=None,
+                module=Backbone(input_size, output_size),
+                in_keys=['observation'],
+                out_keys=['hidden'],
+            ),
+            ProbabilisticActor(
+                spec=None,
+                module=TensorDictModule(
+                    CategoricalSubNet(input_size, output_size),
+                    in_keys=['hidden'],
+                    out_keys=['logits'],
+                ),
+                distribution_class=OneHotCategorical,
+                return_log_prob=False,
+                dist_param_keys=['logits'],
+                out_key_sample=['action'],
+            ),
+            ValueOperator(
+                module=ValueSubNet(input_size, output_size),
+                in_keys=['hidden'],
+            ),
+        )

examples/MAML-RL/helpers/tabular_mdp.py

@@ -38,8 +38,11 @@ class TabularMDPEnv(gym.Env):
         Learning", 2016 (https://arxiv.org/abs/1611.02779)
     """
 
-    def __init__(self, num_states, num_actions, max_episode_steps, seed, task={}):
-        super(TabularMDPEnv, self).__init__()
+    def __init__(self, num_states, num_actions, max_episode_steps, seed, task=None):
+        super().__init__()
+
+        task = task or {}
+
         self.max_episode_steps = max_episode_steps
         self.num_states = num_states
         self.num_actions = num_actions