Deploying to gh-pages from @ 26fb98e 🚀

main/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 1978296481ddd170ce69a8416903ec8e
+config: b9d761ee5ebfc4251fffc3fe599f90db
 tags: d77d1c0d9ca2f4c8421862c7c5a0d620

main/_downloads/433fbd4ad5e11d67afb6f95e0ee37d2b/behavioral_cloning.py

@@ -5,7 +5,7 @@
 # %%%
 # We present here how to perform behavioral cloning on a Minari dataset using `PyTorch <https://pytorch.org/>`_.
 # We will start generating the dataset of the expert policy for the `CartPole-v1 <https://gymnasium.farama.org/environments/classic_control/cart_pole/>`_ environment, which is a classic control problem.
-# The objective is to balance the pole on the cart, and we receive a reward of +1 for each successful timestep.
+# The objective is to balance the pole on the cart, and we receive a reward of +1 for each successful step.
 
 # %%
 # Imports
@@ -108,7 +108,7 @@ def collate_fn(batch):
     return {
         "id": torch.Tensor([x.id for x in batch]),
         "seed": torch.Tensor([x.seed for x in batch]),
-        "total_timesteps": torch.Tensor([x.total_timesteps for x in batch]),
+        "total_steps": torch.Tensor([x.total_steps for x in batch]),
         "observations": torch.nn.utils.rnn.pad_sequence(
             [torch.as_tensor(x.observations) for x in batch],
             batch_first=True

main/_downloads/92aa7a29195623bda8ca31d2e959f2a6/behavioral_cloning.ipynb

@@ -22,7 +22,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We present here how to perform behavioral cloning on a Minari dataset using [PyTorch](https://pytorch.org/).\nWe will start generating the dataset of the expert policy for the [CartPole-v1](https://gymnasium.farama.org/environments/classic_control/cart_pole/) environment, which is a classic control problem.\nThe objective is to balance the pole on the cart, and we receive a reward of +1 for each successful timestep.\n\n"
+        "We present here how to perform behavioral cloning on a Minari dataset using [PyTorch](https://pytorch.org/).\nWe will start generating the dataset of the expert policy for the [CartPole-v1](https://gymnasium.farama.org/environments/classic_control/cart_pole/) environment, which is a classic control problem.\nThe objective is to balance the pole on the cart, and we receive a reward of +1 for each successful step.\n\n"
       ]
     },
     {
@@ -126,7 +126,7 @@
       },
       "outputs": [],
       "source": [
-        "def collate_fn(batch):\n    return {\n        \"id\": torch.Tensor([x.id for x in batch]),\n        \"seed\": torch.Tensor([x.seed for x in batch]),\n        \"total_timesteps\": torch.Tensor([x.total_timesteps for x in batch]),\n        \"observations\": torch.nn.utils.rnn.pad_sequence(\n            [torch.as_tensor(x.observations) for x in batch],\n            batch_first=True\n        ),\n        \"actions\": torch.nn.utils.rnn.pad_sequence(\n            [torch.as_tensor(x.actions) for x in batch],\n            batch_first=True\n        ),\n        \"rewards\": torch.nn.utils.rnn.pad_sequence(\n            [torch.as_tensor(x.rewards) for x in batch],\n            batch_first=True\n        ),\n        \"terminations\": torch.nn.utils.rnn.pad_sequence(\n            [torch.as_tensor(x.terminations) for x in batch],\n            batch_first=True\n        ),\n        \"truncations\": torch.nn.utils.rnn.pad_sequence(\n            [torch.as_tensor(x.truncations) for x in batch],\n            batch_first=True\n        )\n    }"
+        "def collate_fn(batch):\n    return {\n        \"id\": torch.Tensor([x.id for x in batch]),\n        \"seed\": torch.Tensor([x.seed for x in batch]),\n        \"total_steps\": torch.Tensor([x.total_steps for x in batch]),\n        \"observations\": torch.nn.utils.rnn.pad_sequence(\n            [torch.as_tensor(x.observations) for x in batch],\n            batch_first=True\n        ),\n        \"actions\": torch.nn.utils.rnn.pad_sequence(\n            [torch.as_tensor(x.actions) for x in batch],\n            batch_first=True\n        ),\n        \"rewards\": torch.nn.utils.rnn.pad_sequence(\n            [torch.as_tensor(x.rewards) for x in batch],\n            batch_first=True\n        ),\n        \"terminations\": torch.nn.utils.rnn.pad_sequence(\n            [torch.as_tensor(x.terminations) for x in batch],\n            batch_first=True\n        ),\n        \"truncations\": torch.nn.utils.rnn.pad_sequence(\n            [torch.as_tensor(x.truncations) for x in batch],\n            batch_first=True\n        )\n    }"
       ]
     },
     {

main/_modules/minari/data_collector/data_collector/index.html

@@ -361,12 +361,10 @@ <h1>Source code for minari.data_collector.data_collector</h1><div class="highlig
 <span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">annotations</span>
 
 <span class="kn">import</span> <span class="nn">copy</span>
-<span class="kn">import</span> <span class="nn">inspect</span>
 <span class="kn">import</span> <span class="nn">os</span>
 <span class="kn">import</span> <span class="nn">secrets</span>
 <span class="kn">import</span> <span class="nn">shutil</span>
 <span class="kn">import</span> <span class="nn">tempfile</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
 <span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">SupportsFloat</span><span class="p">,</span> <span class="n">Type</span><span class="p">,</span> <span class="n">Union</span>
 
 <span class="kn">import</span> <span class="nn">gymnasium</span> <span class="k">as</span> <span class="nn">gym</span>
@@ -382,6 +380,7 @@ <h1>Source code for minari.data_collector.data_collector</h1><div class="highlig
 <span class="p">)</span>
 <span class="kn">from</span> <span class="nn">minari.dataset.minari_dataset</span> <span class="kn">import</span> <span class="n">MinariDataset</span>
 <span class="kn">from</span> <span class="nn">minari.dataset.minari_storage</span> <span class="kn">import</span> <span class="n">MinariStorage</span>
+<span class="kn">from</span> <span class="nn">minari.utils</span> <span class="kn">import</span> <span class="n">_generate_dataset_metadata</span><span class="p">,</span> <span class="n">_generate_dataset_path</span>
 
 
 <span class="c1"># H5Py supports ints up to uint64</span>
@@ -390,17 +389,6 @@ <h1>Source code for minari.data_collector.data_collector</h1><div class="highlig
 <span class="n">EpisodeBuffer</span> <span class="o">=</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]</span>  <span class="c1"># TODO: narrow this down</span>
 
 
-<span class="k">def</span> <span class="fm">__getattr__</span><span class="p">(</span><span class="n">name</span><span class="p">):</span>
-    <span class="k">if</span> <span class="n">name</span> <span class="o">==</span> <span class="s2">&quot;DataCollectorV0&quot;</span><span class="p">:</span>
-        <span class="n">stacklevel</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">inspect</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
-        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;DataCollectorV0 is deprecated and will be removed. Use DataCollector instead.&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="n">stacklevel</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">DataCollector</span>
-    <span class="k">elif</span> <span class="n">name</span> <span class="o">==</span> <span class="s2">&quot;__path__&quot;</span><span class="p">:</span>
-        <span class="k">return</span> <span class="kc">False</span>  <span class="c1"># see https://stackoverflow.com/a/60803436</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">raise</span> <span class="ne">ImportError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;cannot import name &#39;</span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">&#39; from &#39;</span><span class="si">{</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">&#39; (</span><span class="si">{</span><span class="vm">__file__</span><span class="si">}</span><span class="s2">)&quot;</span><span class="p">)</span>
-
-
 <div class="viewcode-block" id="DataCollector">
 <a class="viewcode-back" href="../../../../api/data_collector/#minari.DataCollector">[docs]</a>
 <span class="k">class</span> <span class="nc">DataCollector</span><span class="p">(</span><span class="n">gym</span><span class="o">.</span><span class="n">Wrapper</span><span class="p">):</span>
@@ -719,8 +707,6 @@ <h1>Source code for minari.data_collector.data_collector</h1><div class="highlig
 <span class="sd">        Returns:</span>
 <span class="sd">            MinariDataset</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="c1"># TODO: move the import to top of the file after removing minari.create_dataset_from_collector_env() in 0.5.0</span>
-        <span class="kn">from</span> <span class="nn">minari.utils</span> <span class="kn">import</span> <span class="n">_generate_dataset_metadata</span><span class="p">,</span> <span class="n">_generate_dataset_path</span>
         <span class="n">dataset_path</span> <span class="o">=</span> <span class="n">_generate_dataset_path</span><span class="p">(</span><span class="n">dataset_id</span><span class="p">)</span>
         <span class="n">metadata</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="n">_generate_dataset_metadata</span><span class="p">(</span>
             <span class="n">dataset_id</span><span class="p">,</span>
@@ -737,7 +723,7 @@ <h1>Source code for minari.data_collector.data_collector</h1><div class="highlig
             <span class="n">minari_version</span><span class="p">,</span>
         <span class="p">)</span>
 
-        <span class="bp">self</span><span class="o">.</span><span class="n">save_to_disk</span><span class="p">(</span><span class="n">dataset_path</span><span class="p">,</span> <span class="n">metadata</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_save_to_disk</span><span class="p">(</span><span class="n">dataset_path</span><span class="p">,</span> <span class="n">metadata</span><span class="p">)</span>
 
         <span class="c1"># will be able to calculate dataset size only after saving the disk, so updating the dataset metadata post `save_to_disk` method</span>
 
@@ -746,7 +732,7 @@ <h1>Source code for minari.data_collector.data_collector</h1><div class="highlig
         <span class="n">dataset</span><span class="o">.</span><span class="n">storage</span><span class="o">.</span><span class="n">update_metadata</span><span class="p">(</span><span class="n">metadata</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">dataset</span>
 
-    <span class="k">def</span> <span class="nf">save_to_disk</span><span class="p">(</span>
+    <span class="k">def</span> <span class="nf">_save_to_disk</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span> <span class="o">|</span> <span class="n">os</span><span class="o">.</span><span class="n">PathLike</span><span class="p">,</span> <span class="n">dataset_metadata</span><span class="p">:</span> <span class="n">Dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
     <span class="p">):</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;Save all in-memory buffer data and move temporary files to a permanent location in disk.</span>
@@ -755,7 +741,6 @@ <h1>Source code for minari.data_collector.data_collector</h1><div class="highlig
 <span class="sd">            path (str): path to store the dataset, e.g.: &#39;/home/foo/datasets/data&#39;</span>
 <span class="sd">            dataset_metadata (Dict, optional): additional metadata to add to the dataset file. Defaults to {}.</span>
 <span class="sd">        &quot;&quot;&quot;</span>
-        <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;This method is deprecated and will become private in v0.5.0.&quot;</span><span class="p">,</span> <span class="ne">DeprecationWarning</span><span class="p">,</span> <span class="n">stacklevel</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_validate_buffer</span><span class="p">()</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_storage</span><span class="o">.</span><span class="n">update_episodes</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span><span class="p">)</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span><span class="o">.</span><span class="n">clear</span><span class="p">()</span>

main/_modules/minari/dataset/episode_data/index.html

@@ -375,7 +375,7 @@ <h1>Source code for minari.dataset.episode_data</h1><div class="highlight"><pre>
 
     <span class="nb">id</span><span class="p">:</span> <span class="nb">int</span>
     <span class="n">seed</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span>
-    <span class="n">total_timesteps</span><span class="p">:</span> <span class="nb">int</span>
+    <span class="n">total_steps</span><span class="p">:</span> <span class="nb">int</span>
     <span class="n">observations</span><span class="p">:</span> <span class="n">Any</span>
     <span class="n">actions</span><span class="p">:</span> <span class="n">Any</span>
     <span class="n">rewards</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span>
@@ -388,7 +388,7 @@ <h1>Source code for minari.dataset.episode_data</h1><div class="highlight"><pre>
             <span class="s2">&quot;EpisodeData(&quot;</span>
             <span class="sa">f</span><span class="s2">&quot;id=</span><span class="si">{</span><span class="nb">repr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">id</span><span class="p">)</span><span class="si">}</span><span class="s2">, &quot;</span>
             <span class="sa">f</span><span class="s2">&quot;seed=</span><span class="si">{</span><span class="nb">repr</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">seed</span><span class="p">)</span><span class="si">}</span><span class="s2">, &quot;</span>
-            <span class="sa">f</span><span class="s2">&quot;total_timesteps=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">total_timesteps</span><span class="si">}</span><span class="s2">, &quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;total_steps=</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">total_steps</span><span class="si">}</span><span class="s2">, &quot;</span>
             <span class="sa">f</span><span class="s2">&quot;observations=</span><span class="si">{</span><span class="n">EpisodeData</span><span class="o">.</span><span class="n">_repr_space_values</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">observations</span><span class="p">)</span><span class="si">}</span><span class="s2">, &quot;</span>
             <span class="sa">f</span><span class="s2">&quot;actions=</span><span class="si">{</span><span class="n">EpisodeData</span><span class="o">.</span><span class="n">_repr_space_values</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">actions</span><span class="p">)</span><span class="si">}</span><span class="s2">, &quot;</span>
             <span class="sa">f</span><span class="s2">&quot;rewards=ndarray of </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rewards</span><span class="p">)</span><span class="si">}</span><span class="s2"> floats, &quot;</span>

main/_modules/minari/dataset/minari_dataset/index.html

@@ -627,7 +627,7 @@ <h1>Source code for minari.dataset.minari_dataset</h1><div class="highlight"><pr
             <span class="k">else</span><span class="p">:</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">_total_steps</span> <span class="o">=</span> <span class="nb">sum</span><span class="p">(</span>
                     <span class="bp">self</span><span class="o">.</span><span class="n">storage</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
-                        <span class="k">lambda</span> <span class="n">episode</span><span class="p">:</span> <span class="n">episode</span><span class="p">[</span><span class="s2">&quot;total_timesteps&quot;</span><span class="p">],</span>
+                        <span class="k">lambda</span> <span class="n">episode</span><span class="p">:</span> <span class="n">episode</span><span class="p">[</span><span class="s2">&quot;total_steps&quot;</span><span class="p">],</span>
                         <span class="n">episode_indices</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">episode_indices</span><span class="p">,</span>
                     <span class="p">)</span>
                 <span class="p">)</span>