upgrade codespell to v2.3.0

adaptive-intelligent-robotics · Sep 11, 2024 · 5eea7db · 5eea7db
1 parent b3d302b
commit 5eea7db
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Showing 4 changed files with 6 additions and 6 deletions.
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -47,7 +47,7 @@ repos:
       - id: mypy
 
 - repo: https://github.com/codespell-project/codespell
-  rev: v2.2.6
+  rev: v2.3.0
   hooks:
     - id: codespell
       name: codespell

diff --git a/examples/dcrlme.ipynb b/examples/dcrlme.ipynb
@@ -15,7 +15,7 @@
     "\n",
     "This notebook shows how to use QDax to find diverse and performing controllers in MDPs with [Descriptor-Conditioned Reinforcement Learning MAP-Elites (DCRL-ME)](https://arxiv.org/abs/2401.08632).\n",
     "This algorithm extends and improves upon [Descriptor-Conditioned Gradients MAP-Elites (DCG-ME)](https://dl.acm.org/doi/abs/10.1145/3583131.3590503)\n",
-    "It can be run locally or on Google Colab. We recommand to use a GPU. This notebook will show:\n",
+    "It can be run locally or on Google Colab. We recommend to use a GPU. This notebook will show:\n",
     "\n",
     "- how to define the problem\n",
     "- how to create the DCRL emitter\n",
@@ -200,7 +200,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Define the fonction to play a step with the policy in the environment\n",
+    "# Define the function to play a step with the policy in the environment\n",
     "def play_step_fn(\n",
     "    env_state: EnvState, policy_params: Params, random_key: RNGKey\n",
     ") -> Tuple[EnvState, Params, RNGKey, DCRLTransition]:\n",

diff --git a/qdax/core/map_elites.py b/qdax/core/map_elites.py
@@ -24,7 +24,7 @@ class MAPElites:
     """Core elements of the MAP-Elites algorithm.
 
     Note: Although very similar to the GeneticAlgorithm, we decided to keep the
-    MAPElites class independant of the GeneticAlgorithm class at the moment to keep
+    MAPElites class independent of the GeneticAlgorithm class at the moment to keep
     elements explicit.
 
     Args:
@@ -65,7 +65,7 @@ def init(
         Args:
             genotypes: initial genotypes, pytree in which leaves
                 have shape (batch_size, num_features)
-            centroids: tesselation centroids of shape (batch_size, num_descriptors)
+            centroids: tessellation centroids of shape (batch_size, num_descriptors)
             random_key: a random key used for stochastic operations.
 
         Returns:

diff --git a/tests/baselines_test/dcrlme_test.py b/tests/baselines_test/dcrlme_test.py
@@ -104,7 +104,7 @@ def test_dcrlme() -> None:
     fake_batch_obs = jnp.zeros(shape=(batch_size, env.observation_size))
     init_params = jax.vmap(policy_network.init)(keys, fake_batch_obs)
 
-    # Define the fonction to play a step with the policy in the environment
+    # Define the function to play a step with the policy in the environment
     def play_step_fn(
         env_state: EnvState, policy_params: Params, random_key: RNGKey
     ) -> Tuple[EnvState, Params, RNGKey, DCRLTransition]: