auto-generating sphinx docs

main/_modules/index.html

@@ -416,6 +416,9 @@ <h1>All modules for which code is available</h1>
 <li><a href="torchrl/data/datasets/openx.html">torchrl.data.datasets.openx</a></li>
 <li><a href="torchrl/data/datasets/roboset.html">torchrl.data.datasets.roboset</a></li>
 <li><a href="torchrl/data/datasets/vd4rl.html">torchrl.data.datasets.vd4rl</a></li>
+<li><a href="torchrl/data/map/hash.html">torchrl.data.map.hash</a></li>
+<li><a href="torchrl/data/map/query.html">torchrl.data.map.query</a></li>
+<li><a href="torchrl/data/map/tdstorage.html">torchrl.data.map.tdstorage</a></li>
 <li><a href="torchrl/data/postprocs/postprocs.html">torchrl.data.postprocs.postprocs</a></li>
 <li><a href="torchrl/data/replay_buffers/checkpointers.html">torchrl.data.replay_buffers.checkpointers</a></li>
 <li><a href="torchrl/data/replay_buffers/replay_buffers.html">torchrl.data.replay_buffers.replay_buffers</a></li>

main/_sources/reference/data.rst.txt

@@ -972,6 +972,26 @@ The following classes are deprecated and just point to the classes above:
     UnboundedContinuousTensorSpec
     UnboundedDiscreteTensorSpec
 
+Trees and Forests
+-----------------
+
+TorchRL offers a set of classes and functions that can be used to represent trees and forests efficiently.
+
+.. currentmodule:: torchrl.data
+
+.. autosummary::
+    :toctree: generated/
+    :template: rl_template.rst
+
+    BinaryToDecimal
+    HashToInt
+    QueryModule
+    RandomProjectionHash
+    SipHash
+    TensorDictMap
+    TensorMap
+
+
 Reinforcement Learning From Human Feedback (RLHF)
 -------------------------------------------------
 

main/_sources/reference/generated/torchrl.data.BinaryToDecimal.rst.txt

@@ -0,0 +1,9 @@
+.. currentmodule:: torchrl.data
+
+
+BinaryToDecimal
+===============
+
+.. autoclass:: BinaryToDecimal
+    :members:
+    :inherited-members:

main/_sources/reference/generated/torchrl.data.HashToInt.rst.txt

@@ -0,0 +1,9 @@
+.. currentmodule:: torchrl.data
+
+
+HashToInt
+=========
+
+.. autoclass:: HashToInt
+    :members:
+    :inherited-members:

main/_sources/reference/generated/torchrl.data.QueryModule.rst.txt

@@ -0,0 +1,9 @@
+.. currentmodule:: torchrl.data
+
+
+QueryModule
+===========
+
+.. autoclass:: QueryModule
+    :members:
+    :inherited-members:

main/_sources/reference/generated/torchrl.data.RandomProjectionHash.rst.txt

@@ -0,0 +1,9 @@
+.. currentmodule:: torchrl.data
+
+
+RandomProjectionHash
+====================
+
+.. autoclass:: RandomProjectionHash
+    :members:
+    :inherited-members:

main/_sources/reference/generated/torchrl.data.SipHash.rst.txt

@@ -0,0 +1,9 @@
+.. currentmodule:: torchrl.data
+
+
+SipHash
+=======
+
+.. autoclass:: SipHash
+    :members:
+    :inherited-members:

main/_sources/reference/generated/torchrl.data.TensorDictMap.rst.txt

@@ -0,0 +1,9 @@
+.. currentmodule:: torchrl.data
+
+
+TensorDictMap
+=============
+
+.. autoclass:: TensorDictMap
+    :members:
+    :inherited-members:

main/_sources/reference/generated/torchrl.data.TensorMap.rst.txt

@@ -0,0 +1,9 @@
+.. currentmodule:: torchrl.data
+
+
+TensorMap
+=========
+
+.. autoclass:: TensorMap
+    :members:
+    :inherited-members:

main/_sources/sg_execution_times.rst.txt

@@ -6,7 +6,7 @@
 
 Computation times
 =================
-**29:59.694** total execution time for 18 files **from all galleries**:
+**27:50.593** total execution time for 18 files **from all galleries**:
 
 .. container::
 
@@ -33,56 +33,56 @@ Computation times
      - Time
      - Mem (MB)
    * - :ref:`sphx_glr_tutorials_torchrl_demo.py` (``reference/generated/tutorials/torchrl_demo.py``)
-     - 03:41.567
-     - 327.6
+     - 03:44.258
+     - 350.1
    * - :ref:`sphx_glr_tutorials_torchrl_envs.py` (``reference/generated/tutorials/torchrl_envs.py``)
-     - 03:26.211
-     - 3045.6
+     - 03:28.778
+     - 4750.9
    * - :ref:`sphx_glr_tutorials_dqn_with_rnn.py` (``reference/generated/tutorials/dqn_with_rnn.py``)
-     - 03:05.467
-     - 2216.0
+     - 03:09.560
+     - 2201.1
    * - :ref:`sphx_glr_tutorials_rb_tutorial.py` (``reference/generated/tutorials/rb_tutorial.py``)
-     - 02:54.738
-     - 487.8
-   * - :ref:`sphx_glr_tutorials_pendulum.py` (``reference/generated/tutorials/pendulum.py``)
-     - 02:50.039
-     - 319.2
+     - 02:56.498
+     - 539.2
    * - :ref:`sphx_glr_tutorials_coding_dqn.py` (``reference/generated/tutorials/coding_dqn.py``)
-     - 02:37.476
-     - 1424.1
+     - 02:40.706
+     - 1101.4
    * - :ref:`sphx_glr_tutorials_coding_ddpg.py` (``reference/generated/tutorials/coding_ddpg.py``)
-     - 01:51.303
-     - 329.6
+     - 01:53.236
+     - 331.2
    * - :ref:`sphx_glr_tutorials_multiagent_ppo.py` (``reference/generated/tutorials/multiagent_ppo.py``)
-     - 01:50.861
-     - 321.3
+     - 01:52.901
+     - 320.9
    * - :ref:`sphx_glr_tutorials_multiagent_competitive_ddpg.py` (``reference/generated/tutorials/multiagent_competitive_ddpg.py``)
-     - 01:30.953
-     - 322.1
+     - 01:31.871
+     - 322.4
    * - :ref:`sphx_glr_tutorials_coding_ppo.py` (``reference/generated/tutorials/coding_ppo.py``)
-     - 01:25.773
+     - 01:26.437
      - 319.9
    * - :ref:`sphx_glr_tutorials_pretrained_models.py` (``reference/generated/tutorials/pretrained_models.py``)
-     - 00:55.958
-     - 2457.2
+     - 00:55.856
+     - 4235.3
    * - :ref:`sphx_glr_tutorials_multi_task.py` (``reference/generated/tutorials/multi_task.py``)
-     - 00:50.697
-     - 321.2
-   * - :ref:`sphx_glr_tutorials_getting-started-0.py` (``reference/generated/tutorials/getting-started-0.py``)
-     - 00:46.306
-     - 319.7
+     - 00:51.256
+     - 320.6
    * - :ref:`sphx_glr_tutorials_getting-started-1.py` (``reference/generated/tutorials/getting-started-1.py``)
-     - 00:46.037
-     - 319.2
+     - 00:46.472
+     - 319.1
+   * - :ref:`sphx_glr_tutorials_getting-started-0.py` (``reference/generated/tutorials/getting-started-0.py``)
+     - 00:46.344
+     - 319.1
    * - :ref:`sphx_glr_tutorials_getting-started-2.py` (``reference/generated/tutorials/getting-started-2.py``)
-     - 00:26.830
-     - 322.4
+     - 00:26.881
+     - 322.3
    * - :ref:`sphx_glr_tutorials_getting-started-5.py` (``reference/generated/tutorials/getting-started-5.py``)
-     - 00:22.736
-     - 322.5
+     - 00:22.901
+     - 323.8
    * - :ref:`sphx_glr_tutorials_getting-started-3.py` (``reference/generated/tutorials/getting-started-3.py``)
-     - 00:22.013
-     - 321.5
+     - 00:22.019
+     - 320.7
+   * - :ref:`sphx_glr_tutorials_pendulum.py` (``reference/generated/tutorials/pendulum.py``)
+     - 00:19.899
+     - 5083.2
    * - :ref:`sphx_glr_tutorials_getting-started-4.py` (``reference/generated/tutorials/getting-started-4.py``)
-     - 00:14.730
-     - 353.0
+     - 00:14.722
+     - 350.7

main/_sources/tutorials/coding_ddpg.rst.txt

@@ -1634,7 +1634,7 @@ modules we need.
 
  .. code-block:: none
 
-      0%|          | 0/10000 [00:00<?, ?it/s]      8%|▊         | 800/10000 [00:00<00:03, 2716.68it/s]     16%|█▌        | 1600/10000 [00:01<00:09, 848.17it/s]     24%|██▍       | 2400/10000 [00:02<00:06, 1207.09it/s]     32%|███▏      | 3200/10000 [00:02<00:04, 1526.43it/s]     40%|████      | 4000/10000 [00:02<00:03, 1790.98it/s]     48%|████▊     | 4800/10000 [00:02<00:02, 2005.88it/s]     56%|█████▌    | 5600/10000 [00:03<00:02, 2164.48it/s]    reward: -2.33 (r0 = -2.14), reward eval: reward:  0.00, reward normalized=-2.40/6.56, grad norm= 139.93, loss_value= 464.94, loss_actor= 13.57, target value: -16.13:  56%|█████▌    | 5600/10000 [00:04<00:02, 2164.48it/s]    reward: -2.33 (r0 = -2.14), reward eval: reward:  0.00, reward normalized=-2.40/6.56, grad norm= 139.93, loss_value= 464.94, loss_actor= 13.57, target value: -16.13:  64%|██████▍   | 6400/10000 [00:05<00:04, 872.98it/s]     reward: -2.21 (r0 = -2.14), reward eval: reward:  0.00, reward normalized=-2.68/5.58, grad norm= 210.01, loss_value= 209.19, loss_actor= 12.35, target value: -17.72:  64%|██████▍   | 6400/10000 [00:06<00:04, 872.98it/s]    reward: -2.21 (r0 = -2.14), reward eval: reward:  0.00, reward normalized=-2.68/5.58, grad norm= 210.01, loss_value= 209.19, loss_actor= 12.35, target value: -17.72:  72%|███████▏  | 7200/10000 [00:07<00:04, 622.45it/s]    reward: -5.11 (r0 = -2.14), reward eval: reward:  0.00, reward normalized=-2.35/6.39, grad norm= 114.10, loss_value= 309.52, loss_actor= 16.71, target value: -16.61:  72%|███████▏  | 7200/10000 [00:09<00:04, 622.45it/s]    reward: -5.11 (r0 = -2.14), reward eval: reward:  0.00, reward normalized=-2.35/6.39, grad norm= 114.10, loss_value= 309.52, loss_actor= 16.71, target value: -16.61:  80%|████████  | 8000/10000 [00:09<00:03, 520.57it/s]    reward: -4.88 (r0 = -2.14), reward eval: reward:  0.00, reward normalized=-3.06/5.66, grad norm= 137.25, loss_value= 258.80, loss_actor= 20.02, target value: -20.70:  80%|████████  | 8000/10000 [00:11<00:03, 520.57it/s]    reward: -4.88 (r0 = -2.14), reward eval: reward:  0.00, reward normalized=-3.06/5.66, grad norm= 137.25, loss_value= 258.80, loss_actor= 20.02, target value: -20.70:  88%|████████▊ | 8800/10000 [00:11<00:02, 469.19it/s]    reward: -9.04 (r0 = -2.14), reward eval: reward:  1.64, reward normalized=-3.46/6.15, grad norm= 145.55, loss_value= 367.34, loss_actor= 22.95, target value: -24.53:  88%|████████▊ | 8800/10000 [00:14<00:02, 469.19it/s]    reward: -9.04 (r0 = -2.14), reward eval: reward:  1.64, reward normalized=-3.46/6.15, grad norm= 145.55, loss_value= 367.34, loss_actor= 22.95, target value: -24.53:  96%|█████████▌| 9600/10000 [00:14<00:01, 374.35it/s]    reward: -5.03 (r0 = -2.14), reward eval: reward:  1.64, reward normalized=-4.19/6.89, grad norm= 453.91, loss_value= 360.29, loss_actor= 23.64, target value: -28.83:  96%|█████████▌| 9600/10000 [00:16<00:01, 374.35it/s]    reward: -5.03 (r0 = -2.14), reward eval: reward:  1.64, reward normalized=-4.19/6.89, grad norm= 453.91, loss_value= 360.29, loss_actor= 23.64, target value: -28.83: : 10400it [00:17, 343.88it/s]                            reward: -2.48 (r0 = -2.14), reward eval: reward:  1.64, reward normalized=-3.64/6.04, grad norm= 260.25, loss_value= 369.91, loss_actor= 20.86, target value: -26.16: : 10400it [00:19, 343.88it/s]
+      0%|          | 0/10000 [00:00<?, ?it/s]      8%|▊         | 800/10000 [00:00<00:03, 2533.63it/s]     16%|█▌        | 1600/10000 [00:01<00:10, 827.75it/s]     24%|██▍       | 2400/10000 [00:02<00:06, 1184.15it/s]     32%|███▏      | 3200/10000 [00:02<00:04, 1482.38it/s]     40%|████      | 4000/10000 [00:02<00:03, 1727.05it/s]     48%|████▊     | 4800/10000 [00:03<00:02, 1932.28it/s]     56%|█████▌    | 5600/10000 [00:03<00:02, 2091.49it/s]    reward: -2.23 (r0 = -1.88), reward eval: reward:  0.00, reward normalized=-1.85/5.95, grad norm= 115.03, loss_value= 223.69, loss_actor= 14.65, target value: -12.77:  56%|█████▌    | 5600/10000 [00:05<00:02, 2091.49it/s]    reward: -2.23 (r0 = -1.88), reward eval: reward:  0.00, reward normalized=-1.85/5.95, grad norm= 115.03, loss_value= 223.69, loss_actor= 14.65, target value: -12.77:  64%|██████▍   | 6400/10000 [00:05<00:04, 837.12it/s]     reward: -2.76 (r0 = -1.88), reward eval: reward:  0.00, reward normalized=-2.45/5.53, grad norm= 49.43, loss_value= 213.72, loss_actor= 15.69, target value: -16.02:  64%|██████▍   | 6400/10000 [00:07<00:04, 837.12it/s]     reward: -2.76 (r0 = -1.88), reward eval: reward:  0.00, reward normalized=-2.45/5.53, grad norm= 49.43, loss_value= 213.72, loss_actor= 15.69, target value: -16.02:  72%|███████▏  | 7200/10000 [00:07<00:04, 595.56it/s]    reward: -4.17 (r0 = -1.88), reward eval: reward:  0.00, reward normalized=-2.85/5.99, grad norm= 60.16, loss_value= 295.90, loss_actor= 18.57, target value: -19.15:  72%|███████▏  | 7200/10000 [00:09<00:04, 595.56it/s]    reward: -4.17 (r0 = -1.88), reward eval: reward:  0.00, reward normalized=-2.85/5.99, grad norm= 60.16, loss_value= 295.90, loss_actor= 18.57, target value: -19.15:  80%|████████  | 8000/10000 [00:09<00:04, 495.01it/s]    reward: -3.54 (r0 = -1.88), reward eval: reward:  0.00, reward normalized=-2.78/5.50, grad norm= 93.13, loss_value= 222.20, loss_actor= 17.51, target value: -19.17:  80%|████████  | 8000/10000 [00:11<00:04, 495.01it/s]    reward: -3.54 (r0 = -1.88), reward eval: reward:  0.00, reward normalized=-2.78/5.50, grad norm= 93.13, loss_value= 222.20, loss_actor= 17.51, target value: -19.17:  88%|████████▊ | 8800/10000 [00:12<00:02, 447.78it/s]    reward: -3.94 (r0 = -1.88), reward eval: reward: -5.05, reward normalized=-3.18/5.21, grad norm= 73.80, loss_value= 216.62, loss_actor= 21.77, target value: -23.01:  88%|████████▊ | 8800/10000 [00:14<00:02, 447.78it/s]    reward: -3.94 (r0 = -1.88), reward eval: reward: -5.05, reward normalized=-3.18/5.21, grad norm= 73.80, loss_value= 216.62, loss_actor= 21.77, target value: -23.01:  96%|█████████▌| 9600/10000 [00:15<00:01, 356.23it/s]    reward: -1.04 (r0 = -1.88), reward eval: reward: -5.05, reward normalized=-3.08/5.53, grad norm= 191.09, loss_value= 228.22, loss_actor= 18.82, target value: -21.64:  96%|█████████▌| 9600/10000 [00:17<00:01, 356.23it/s]    reward: -1.04 (r0 = -1.88), reward eval: reward: -5.05, reward normalized=-3.08/5.53, grad norm= 191.09, loss_value= 228.22, loss_actor= 18.82, target value: -21.64: : 10400it [00:18, 327.83it/s]                            reward: -4.56 (r0 = -1.88), reward eval: reward: -5.05, reward normalized=-3.14/5.20, grad norm= 203.16, loss_value= 257.88, loss_actor= 20.11, target value: -22.75: : 10400it [00:20, 327.83it/s]
 
 
 
@@ -1704,9 +1704,9 @@ To iterate further on this loss module we might consider:
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** (1 minutes 51.303 seconds)
+   **Total running time of the script:** (1 minutes 53.236 seconds)
 
-**Estimated memory usage:**  330 MB
+**Estimated memory usage:**  331 MB
 
 
 .. _sphx_glr_download_tutorials_coding_ddpg.py: