diff --git a/navix/_version.py b/navix/_version.py
index 6006f8f..9026dd1 100644
--- a/navix/_version.py
+++ b/navix/_version.py
@@ -18,5 +18,5 @@
 # under the License.
 
 
-__version__ = "0.6.0"
+__version__ = "0.6.1"
 __version_info__ = tuple(int(i) for i in __version__.split(".") if i.isdigit())
diff --git a/navix/agents/models.py b/navix/agents/models.py
index f743d77..b9e3b36 100644
--- a/navix/agents/models.py
+++ b/navix/agents/models.py
@@ -1,3 +1,4 @@
+from typing import Tuple
 from jax import Array
 import jax.numpy as jnp
 import distrax
@@ -5,6 +6,18 @@
 from flax.linen.initializers import constant, orthogonal
 
 
+RNNState = tuple
+
+
+class DenseRNN(nn.Dense):
+    """A linear module that returns an empty RNN state,
+    which makes it behave like an RNN layer."""
+
+    @nn.compact
+    def __call__(self, carry: RNNState, x: Array) -> Tuple[RNNState, Array]:
+        return (), super().__call__(x)
+
+
 class MLPEncoder(nn.Module):
     hidden_size: int = 64
 
@@ -66,11 +79,66 @@ def setup(self):
             ]
         )
 
-    def __call__(self, x):
+    def __call__(self, x: Array) -> Tuple[distrax.Distribution, Array]:
         return distrax.Categorical(self.actor(x)), jnp.squeeze(self.critic(x), -1)
 
     def policy(self, x: Array) -> distrax.Distribution:
         return distrax.Categorical(logits=self.actor(x))
-    
+
     def value(self, x: Array) -> Array:
         return jnp.squeeze(self.critic(x), -1)
+
+
+class ActorCriticRnn(nn.Module):
+    action_dim: int
+    actor_encoder: nn.Module = MLPEncoder()
+    critic_encoder: nn.Module = MLPEncoder()
+    hidden_size: int = 64
+    recurrent: bool = False
+
+    def setup(self):
+        self.actor = nn.Sequential(
+            [
+                self.actor_encoder,
+                nn.Dense(
+                    self.action_dim,
+                    kernel_init=orthogonal(0.01),
+                    bias_init=constant(0.0),
+                ),
+                # lambda x: distrax.Categorical(logits=x),
+            ]
+        )
+
+        self.critic = nn.Sequential(
+            [
+                self.critic_encoder,
+                nn.Dense(1, kernel_init=orthogonal(1.0), bias_init=constant(0.0)),
+                # lambda x: jnp.squeeze(x, axis=-1),
+            ]
+        )
+
+        if self.recurrent:
+            self.core_actor = nn.LSTMCell(self.hidden_size)
+            self.core_critic = nn.LSTMCell(self.hidden_size)
+        else:
+            self.core_actor = DenseRNN(self.hidden_size)
+            self.core_critic = DenseRNN(self.hidden_size)
+
+    def __call__(
+        self, x: Array, carry: RNNState
+    ) -> Tuple[RNNState, Tuple[distrax.Distribution, Array]]:
+        pi = distrax.Categorical(logits=self.actor(x))
+        v = jnp.squeeze(self.critic(x), -1)
+        return carry, (pi, v)
+
+    def policy(
+        self, carry: RNNState, x: Array
+    ) -> Tuple[RNNState, distrax.Distribution]:
+        actor_embed = self.actor_encoder(x)
+        carry, actor_embed = self.core_actor(carry, actor_embed)
+        return carry, distrax.Categorical(logits=self.actor(x))
+
+    def value(self, x: Array, carry: RNNState) -> Tuple[RNNState, Array]:
+        critic_embed = self.critic_encoder(x)
+        carry, critic_embed = self.core_critic(carry, critic_embed)
+        return carry, jnp.squeeze(self.critic(x), axis=-1)