chore: uniform sac network sizes

examples/dads.ipynb

@@ -116,7 +116,8 @@
     "alpha_init = 1.0 #@param {type:\"number\"}\n",
     "discount = 0.97 #@param {type:\"number\"}\n",
     "reward_scaling = 1.0 #@param {type:\"number\"}\n",
-    "hidden_layer_sizes = (256, 256) #@param {type:\"raw\"}\n",
+    "critic_hidden_layer_size = (256, 256) #@param {type:\"raw\"}\n",
+    "policy_hidden_layer_size = (256, 256) #@param {type:\"raw\"}\n",
     "fix_alpha = False #@param {type:\"boolean\"}\n",
     "normalize_observations = False #@param {type:\"boolean\"}\n",
     "# DADS config\n",
@@ -202,7 +203,8 @@
     "    alpha_init=alpha_init,\n",
     "    discount=discount,\n",
     "    reward_scaling=reward_scaling,\n",
-    "    hidden_layer_sizes=hidden_layer_sizes,\n",
+    "    critic_hidden_layer_size=critic_hidden_layer_size,\n",
+    "    policy_hidden_layer_size=policy_hidden_layer_size,\n",
     "    fix_alpha=fix_alpha,\n",
     "    # DADS config\n",
     "    num_skills=num_skills,\n",
@@ -520,11 +522,8 @@
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "9ae46cf6a59eb5e192bc4f27fbb5c33d8a30eb9acb43edbb510eeaf7c819ab64"
-  },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.2 64-bit ('3.9.2')",
    "language": "python",
    "name": "python3"
   },
@@ -538,7 +537,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.9.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "1508da3da994e8b4133db52fbfc99c200ce19b8717cb4612fe84174533968534"
+   }
   }
  },
  "nbformat": 4,

examples/diayn.ipynb

@@ -116,7 +116,8 @@
     "alpha_init = 1.0 #@param {type:\"number\"}\n",
     "discount = 0.97 #@param {type:\"number\"}\n",
     "reward_scaling = 1.0 #@param {type:\"number\"}\n",
-    "hidden_layer_sizes = (256, 256) #@param {type:\"raw\"}\n",
+    "critic_hidden_layer_size = (256, 256) #@param {type:\"raw\"}\n",
+    "policy_hidden_layer_size = (256, 256) #@param {type:\"raw\"}\n",
     "fix_alpha = False #@param {type:\"boolean\"}\n",
     "normalize_observations = False #@param {type:\"boolean\"}\n",
     "# DIAYN config\n",
@@ -200,7 +201,8 @@
     "    alpha_init=alpha_init,\n",
     "    discount=discount,\n",
     "    reward_scaling=reward_scaling,\n",
-    "    hidden_layer_sizes=hidden_layer_sizes,\n",
+    "    critic_hidden_layer_size=critic_hidden_layer_size,\n",
+    "    policy_hidden_layer_size=policy_hidden_layer_size,\n",
     "    fix_alpha=fix_alpha,\n",
     "    # DIAYN config\n",
     "    num_skills=num_skills,\n",
@@ -510,11 +512,8 @@
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "9ae46cf6a59eb5e192bc4f27fbb5c33d8a30eb9acb43edbb510eeaf7c819ab64"
-  },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.2 64-bit ('3.9.2')",
    "language": "python",
    "name": "python3"
   },
@@ -528,7 +527,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.9.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "1508da3da994e8b4133db52fbfc99c200ce19b8717cb4612fe84174533968534"
+   }
   }
  },
  "nbformat": 4,

examples/me_sac_pbt.ipynb

@@ -63,7 +63,8 @@
     "episode_length = 1000\n",
     "tau = 0.005\n",
     "alpha_init = 1.0\n",
-    "hidden_layer_sizes = (256, 256)\n",
+    "critic_hidden_layer_size = (256, 256) \n",
+    "policy_hidden_layer_size = (256, 256) \n",
     "fix_alpha = False\n",
     "normalize_observations = False\n",
     "\n",
@@ -148,7 +149,8 @@
     "    tau=tau,\n",
     "    normalize_observations=normalize_observations,\n",
     "    alpha_init=alpha_init,\n",
-    "    hidden_layer_sizes=hidden_layer_sizes,\n",
+    "    critic_hidden_layer_size=critic_hidden_layer_size,\n",
+    "    policy_hidden_layer_size=policy_hidden_layer_size,\n",
     "    fix_alpha=fix_alpha,\n",
     ")\n",
     "\n",
@@ -527,11 +529,8 @@
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "9ae46cf6a59eb5e192bc4f27fbb5c33d8a30eb9acb43edbb510eeaf7c819ab64"
-  },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.2 64-bit ('3.9.2')",
    "language": "python",
    "name": "python3"
   },
@@ -545,7 +544,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.16"
+   "version": "3.9.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "1508da3da994e8b4133db52fbfc99c200ce19b8717cb4612fe84174533968534"
+   }
   }
  },
  "nbformat": 4,

examples/sac_pbt.ipynb

@@ -95,7 +95,8 @@
     "grad_updates_per_step = 1.0\n",
     "tau = 0.005\n",
     "alpha_init = 1.0\n",
-    "hidden_layer_sizes = (256, 256)\n",
+    "critic_hidden_layer_size = (256, 256) \n",
+    "policy_hidden_layer_size = (256, 256)\n",
     "fix_alpha = False\n",
     "normalize_observations = False\n",
     "\n",
@@ -217,7 +218,8 @@
     "    tau=tau,\n",
     "    normalize_observations=normalize_observations,\n",
     "    alpha_init=alpha_init,\n",
-    "    hidden_layer_sizes=hidden_layer_sizes,\n",
+    "    critic_hidden_layer_size=critic_hidden_layer_size,\n",
+    "    policy_hidden_layer_size=policy_hidden_layer_size,\n",
     "    fix_alpha=fix_alpha,\n",
     ")\n",
     "\n",
@@ -544,7 +546,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.2 64-bit ('3.9.2')",
    "language": "python",
    "name": "python3"
   },
@@ -558,7 +560,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.9.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "1508da3da994e8b4133db52fbfc99c200ce19b8717cb4612fe84174533968534"
+   }
   }
  },
  "nbformat": 4,

examples/smerl.ipynb

@@ -117,7 +117,8 @@
     "alpha_init = 1.0 #@param {type:\"number\"}\n",
     "discount = 0.97 #@param {type:\"number\"}\n",
     "reward_scaling = 1.0 #@param {type:\"number\"}\n",
-    "hidden_layer_sizes = (256, 256) #@param {type:\"raw\"}\n",
+    "critic_hidden_layer_size = (256, 256) #@param {type:\"raw\"}\n",
+    "policy_hidden_layer_size = (256, 256) #@param {type:\"raw\"}\n",
     "fix_alpha = False #@param {type:\"boolean\"}\n",
     "normalize_observations = False #@param {type:\"boolean\"}\n",
     "# DIAYN config\n",
@@ -212,7 +213,8 @@
     "    alpha_init=alpha_init,\n",
     "    discount=discount,\n",
     "    reward_scaling=reward_scaling,\n",
-    "    hidden_layer_sizes=hidden_layer_sizes,\n",
+    "    critic_hidden_layer_size=critic_hidden_layer_size,\n",
+    "    policy_hidden_layer_size=policy_hidden_layer_size,\n",
     "    fix_alpha=fix_alpha,\n",
     "    # DIAYN config\n",
     "    num_skills=num_skills,\n",
@@ -525,11 +527,8 @@
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "9ae46cf6a59eb5e192bc4f27fbb5c33d8a30eb9acb43edbb510eeaf7c819ab64"
-  },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3.9.2 64-bit ('3.9.2')",
    "language": "python",
    "name": "python3"
   },
@@ -543,7 +542,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.9.2"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "1508da3da994e8b4133db52fbfc99c200ce19b8717cb4612fe84174533968534"
+   }
   }
  },
  "nbformat": 4,

qdax/baselines/dads.py

@@ -84,6 +84,8 @@ def __init__(self, config: DadsConfig, action_size: int, descriptor_size: int):
             action_size=action_size,
             descriptor_size=descriptor_size,
             omit_input_dynamics_dim=config.omit_input_dynamics_dim,
+            policy_hidden_layer_size=config.policy_hidden_layer_size,
+            critic_hidden_layer_size=config.critic_hidden_layer_size,
         )
 
         # define the action distribution

qdax/baselines/diayn.py

@@ -78,7 +78,8 @@ def __init__(self, config: DiaynConfig, action_size: int):
         self._policy, self._critic, self._discriminator = make_diayn_networks(
             num_skills=self._config.num_skills,
             action_size=action_size,
-            hidden_layer_sizes=self._config.hidden_layer_sizes,
+            policy_hidden_layer_size=self._config.policy_hidden_layer_size,
+            critic_hidden_layer_size=self._config.critic_hidden_layer_size,
         )
 
         # define the action distribution

qdax/baselines/sac.py

@@ -71,7 +71,8 @@ class SacConfig:
     alpha_init: float = 1.0
     discount: float = 0.97
     reward_scaling: float = 1.0
-    hidden_layer_sizes: tuple = (256, 256)
+    critic_hidden_layer_size: tuple = (256, 256)
+    policy_hidden_layer_size: tuple = (256, 256)
     fix_alpha: bool = False
 
 
@@ -82,7 +83,9 @@ def __init__(self, config: SacConfig, action_size: int) -> None:
 
         # define the networks
         self._policy, self._critic = make_sac_networks(
-            action_size=action_size, hidden_layer_sizes=self._config.hidden_layer_sizes
+            action_size=action_size,
+            critic_hidden_layer_size=self._config.critic_hidden_layer_size,
+            policy_hidden_layer_size=self._config.policy_hidden_layer_size,
         )
 
         # define the action distribution

qdax/baselines/sac_pbt.py

@@ -110,7 +110,8 @@ class PBTSacConfig:
     tau: float = 0.005
     normalize_observations: bool = False
     alpha_init: float = 1.0
-    hidden_layer_sizes: tuple = (256, 256)
+    policy_hidden_layer_size: tuple = (256, 256)
+    critic_hidden_layer_size: tuple = (256, 256)
     fix_alpha: bool = False
 
 
@@ -123,7 +124,8 @@ def __init__(self, config: PBTSacConfig, action_size: int) -> None:
             tau=config.tau,
             normalize_observations=config.normalize_observations,
             alpha_init=config.alpha_init,
-            hidden_layer_sizes=config.hidden_layer_sizes,
+            policy_hidden_layer_size=config.policy_hidden_layer_size,
+            critic_hidden_layer_size=config.critic_hidden_layer_size,
             fix_alpha=config.fix_alpha,
             # unused default values for parameters that will be learnt as part of PBT
             learning_rate=3e-4,

qdax/core/neuroevolution/networks/dads_networks.py

@@ -126,7 +126,8 @@ def __call__(
 def make_dads_networks(
     action_size: int,
     descriptor_size: int,
-    hidden_layer_sizes: Tuple[int, ...] = (256, 256),
+    critic_hidden_layer_size: Tuple[int, ...] = (256, 256),
+    policy_hidden_layer_size: Tuple[int, ...] = (256, 256),
     omit_input_dynamics_dim: int = 2,
     identity_covariance: bool = True,
     dynamics_initializer: Optional[Initializer] = None,
@@ -155,7 +156,7 @@ def _actor_fn(obs: Observation) -> jnp.ndarray:
         network = hk.Sequential(
             [
                 hk.nets.MLP(
-                    list(hidden_layer_sizes) + [2 * action_size],
+                    list(policy_hidden_layer_size) + [2 * action_size],
                     w_init=hk.initializers.VarianceScaling(1.0, "fan_in", "uniform"),
                     activation=jax.nn.relu,
                 ),
@@ -167,7 +168,7 @@ def _critic_fn(obs: Observation, action: Action) -> jnp.ndarray:
         network1 = hk.Sequential(
             [
                 hk.nets.MLP(
-                    list(hidden_layer_sizes) + [1],
+                    list(critic_hidden_layer_size) + [1],
                     w_init=hk.initializers.VarianceScaling(1.0, "fan_in", "uniform"),
                     activation=jax.nn.relu,
                 ),
@@ -176,7 +177,7 @@ def _critic_fn(obs: Observation, action: Action) -> jnp.ndarray:
         network2 = hk.Sequential(
             [
                 hk.nets.MLP(
-                    list(hidden_layer_sizes) + [1],
+                    list(critic_hidden_layer_size) + [1],
                     w_init=hk.initializers.VarianceScaling(1.0, "fan_in", "uniform"),
                     activation=jax.nn.relu,
                 ),
@@ -191,7 +192,7 @@ def _dynamics_fn(
         obs: StateDescriptor, skill: Skill, target: StateDescriptor
     ) -> jnp.ndarray:
         dynamics_network = DynamicsNetwork(
-            hidden_layer_sizes,
+            critic_hidden_layer_size,
             descriptor_size,
             omit_input_dynamics_dim=omit_input_dynamics_dim,
             identity_covariance=identity_covariance,

qdax/core/neuroevolution/networks/diayn_networks.py

@@ -10,7 +10,8 @@
 def make_diayn_networks(
     action_size: int,
     num_skills: int,
-    hidden_layer_sizes: Tuple[int, ...] = (256, 256),
+    critic_hidden_layer_size: Tuple[int, ...] = (256, 256),
+    policy_hidden_layer_size: Tuple[int, ...] = (256, 256),
 ) -> Tuple[hk.Transformed, hk.Transformed, hk.Transformed]:
     """Creates networks used in DIAYN.
 
@@ -30,7 +31,7 @@ def _actor_fn(obs: Observation) -> jnp.ndarray:
         network = hk.Sequential(
             [
                 hk.nets.MLP(
-                    list(hidden_layer_sizes) + [2 * action_size],
+                    list(policy_hidden_layer_size) + [2 * action_size],
                     w_init=hk.initializers.VarianceScaling(1.0, "fan_in", "uniform"),
                     activation=jax.nn.relu,
                 ),
@@ -42,7 +43,7 @@ def _critic_fn(obs: Observation, action: Action) -> jnp.ndarray:
         network1 = hk.Sequential(
             [
                 hk.nets.MLP(
-                    list(hidden_layer_sizes) + [1],
+                    list(critic_hidden_layer_size) + [1],
                     w_init=hk.initializers.VarianceScaling(1.0, "fan_in", "uniform"),
                     activation=jax.nn.relu,
                 ),
@@ -51,7 +52,7 @@ def _critic_fn(obs: Observation, action: Action) -> jnp.ndarray:
         network2 = hk.Sequential(
             [
                 hk.nets.MLP(
-                    list(hidden_layer_sizes) + [1],
+                    list(critic_hidden_layer_size) + [1],
                     w_init=hk.initializers.VarianceScaling(1.0, "fan_in", "uniform"),
                     activation=jax.nn.relu,
                 ),
@@ -66,7 +67,7 @@ def _discriminator_fn(obs: Observation) -> jnp.ndarray:
         network = hk.Sequential(
             [
                 hk.nets.MLP(
-                    list(hidden_layer_sizes) + [num_skills],
+                    list(critic_hidden_layer_size) + [num_skills],
                     w_init=hk.initializers.VarianceScaling(1.0, "fan_in", "uniform"),
                     activation=jax.nn.relu,
                 ),