Edan toledo/issue112

README.md

@@ -199,11 +199,9 @@ If you use Stoix in your work, please cite us:
 ```bibtex
 @software{toledo2024stoix,
 author = {Toledo, Edan},
-doi = {10.5281/zenodo.10916258},
-month = apr,
+doi = {10.5281/zenodo.10916257},
 title = {{Stoix: Distributed Single-Agent Reinforcement Learning End-to-End in JAX}},
 url = {https://github.com/EdanToledo/Stoix},
-version = {v0.0.1},
 year = {2024}
 }
 ```

stoix/configs/network/chained_torsos.yaml

@@ -0,0 +1,40 @@
+# ---Example of chaining arbitrary torsos---
+actor_network:
+  pre_torso:
+    _target_: stoix.networks.base.chained_torsos # we call the chained torsos creation function
+    _recursive_: false  # we disable recursive instantiation for this object as we do the creation manually
+    torso_cfgs:
+
+      - _target_: stoix.networks.resnet.ResNetTorso
+        hidden_units_per_group: [64, 64]
+        blocks_per_group: [1, 1]
+        use_layer_norm: False
+        activation: silu
+
+      - _target_: stoix.networks.torso.MLPTorso
+        layer_sizes: [64, 64]
+        use_layer_norm: False
+        activation: relu
+
+  action_head:
+    _target_: stoix.networks.heads.CategoricalHead
+
+critic_network:
+  pre_torso:
+    _target_: stoix.networks.base.chained_torsos # we call the chained torsos creation function
+    _recursive_: false # we disable recursive instantiation for this object as we do the creation manually
+    torso_cfgs:
+
+      - _target_: stoix.networks.resnet.ResNetTorso
+        hidden_units_per_group: [64, 64]
+        blocks_per_group: [1, 1]
+        use_layer_norm: False
+        activation: silu
+
+      - _target_: stoix.networks.torso.MLPTorso
+        layer_sizes: [64, 64]
+        use_layer_norm: False
+        activation: relu
+
+  critic_head:
+    _target_: stoix.networks.heads.ScalarCriticHead

stoix/networks/base.py

@@ -1,8 +1,9 @@
 import functools
-from typing import Sequence, Tuple, Union
+from typing import Any, Dict, List, Sequence, Tuple, Union
 
 import chex
 import distrax
+import hydra
 import jax
 import jax.numpy as jnp
 import numpy as np
@@ -182,3 +183,16 @@ def __call__(
         critic_output = self.critic_head(critic_output)
 
         return critic_hidden_state, critic_output
+
+
+def chained_torsos(torso_cfgs: List[Dict[str, Any]]) -> nn.Module:
+    """Create a network by chaining multiple torsos together using a list of configs.
+    This makes use of hydra to instantiate the modules and the composite network
+    to chain them together.
+
+    Args:
+        torso_cfgs: List of dictionaries containing the configuration for each torso.
+            These configs should use the same format as the individual torso configs."""
+
+    torso_modules = [hydra.utils.instantiate(torso_cfg) for torso_cfg in torso_cfgs]
+    return CompositeNetwork(torso_modules)