From 60543bf577c8d89321745231b0e8c1723a888971 Mon Sep 17 00:00:00 2001 From: younik <42100908+younik@users.noreply.github.com> Date: Thu, 1 Feb 2024 20:29:29 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20Farama-F?= =?UTF-8?q?oundation/Minari@26fb98edec72aba9de4dea6811240c690bfadf7d=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- main/.buildinfo | 2 +- .../tutorials_python.zip | Bin 57251 -> 57239 bytes .../behavioral_cloning.py | 4 +- .../behavioral_cloning.ipynb | 4 +- .../tutorials_jupyter.zip | Bin 77453 -> 77441 bytes .../data_collector/data_collector/index.html | 21 +----- .../minari/dataset/episode_data/index.html | 4 +- .../minari/dataset/minari_dataset/index.html | 2 +- main/_modules/minari/utils/index.html | 60 +----------------- main/api/data_collector/index.html | 16 ++--- .../minari_dataset/episode_data/index.html | 14 ++-- main/content/dataset_standards/index.html | 18 +++--- .../datasets/antmaze/large-diverse/index.html | 2 +- main/datasets/antmaze/large-play/index.html | 2 +- .../antmaze/medium-diverse/index.html | 2 +- main/datasets/antmaze/medium-play/index.html | 2 +- .../datasets/antmaze/umaze-diverse/index.html | 2 +- main/datasets/antmaze/umaze/index.html | 2 +- main/datasets/door/cloned/index.html | 2 +- main/datasets/door/expert/index.html | 2 +- main/datasets/door/human/index.html | 2 +- main/datasets/hammer/cloned/index.html | 2 +- main/datasets/hammer/expert/index.html | 2 +- main/datasets/hammer/human/index.html | 2 +- main/datasets/kitchen/complete/index.html | 2 +- main/datasets/kitchen/mixed/index.html | 2 +- main/datasets/kitchen/partial/index.html | 2 +- .../minigrid/fourrooms-random/index.html | 4 +- main/datasets/minigrid/fourrooms/index.html | 4 +- main/datasets/pen/cloned/index.html | 2 +- main/datasets/pen/expert/index.html | 2 +- main/datasets/pen/human/index.html | 2 +- .../datasets/pointmaze/large-dense/index.html | 2 +- main/datasets/pointmaze/large/index.html | 2 +- .../pointmaze/medium-dense/index.html | 2 +- main/datasets/pointmaze/medium/index.html | 2 +- main/datasets/pointmaze/open-dense/index.html | 2 +- main/datasets/pointmaze/open/index.html | 2 +- .../datasets/pointmaze/umaze-dense/index.html | 2 +- main/datasets/pointmaze/umaze/index.html | 2 +- main/datasets/relocate/cloned/index.html | 2 +- main/datasets/relocate/expert/index.html | 2 +- main/datasets/relocate/human/index.html | 2 +- main/genindex/index.html | 8 ++- main/objects.inv | Bin 1827 -> 1824 bytes main/searchindex.js | 2 +- .../behavioral_cloning/index.html | 4 +- 47 files changed, 77 insertions(+), 148 deletions(-) diff --git a/main/.buildinfo b/main/.buildinfo index 9e8f7cbb..eda4c395 100644 --- a/main/.buildinfo +++ b/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 diff --git a/main/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip b/main/_downloads/315c4c52fb68082a731b192d944e2ede/tutorials_python.zip index 2a419d70f34727d75f9d1a03b9e7ece327da4c0e..47636b5f4914641808891a45b3b4f7aaadb608b4 100644 GIT binary patch delta 236 zcmZ3ypLzOzX5IjAW)=|!5XfKbxVci*&zl*Nu6vWub>*p;5q$5_HjMRyPcah@S-?x^51)|@*t6=g=f}V2Qx5$uq=ws+{uae4cH#v1qlKGv?x@i diff --git a/main/_downloads/433fbd4ad5e11d67afb6f95e0ee37d2b/behavioral_cloning.py b/main/_downloads/433fbd4ad5e11d67afb6f95e0ee37d2b/behavioral_cloning.py index 219d2f73..403e6040 100644 --- a/main/_downloads/433fbd4ad5e11d67afb6f95e0ee37d2b/behavioral_cloning.py +++ b/main/_downloads/433fbd4ad5e11d67afb6f95e0ee37d2b/behavioral_cloning.py @@ -5,7 +5,7 @@ # %%% # We present here how to perform behavioral cloning on a Minari dataset using `PyTorch `_. # We will start generating the dataset of the expert policy for the `CartPole-v1 `_ 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 diff --git a/main/_downloads/92aa7a29195623bda8ca31d2e959f2a6/behavioral_cloning.ipynb b/main/_downloads/92aa7a29195623bda8ca31d2e959f2a6/behavioral_cloning.ipynb index ccdaba37..25481d12 100644 --- a/main/_downloads/92aa7a29195623bda8ca31d2e959f2a6/behavioral_cloning.ipynb +++ b/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 }" ] }, { diff --git a/main/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip b/main/_downloads/a5659940aa3f8f568547d47752a43172/tutorials_jupyter.zip index 73ff6cc811d0d6b4d321cf88406c62de84ca54a8..47bd9d86205c5fcfed0ebd566cf1df1c00358629 100644 GIT binary patch delta 221 zcmeCZ%hGt4g*U*PnMH&F1Xe6|+$?4p62c5(>=utu1Ti-9t`-*p(h+Lf8!w+$V_*Pb zb%tqrjGCL5_J%QU{y)K7c(T0Y{w_F#dtIzWL*xX1)NGmDvqfoowjQGZ+ddseposwdSyMv* diff --git a/main/_modules/minari/data_collector/data_collector/index.html b/main/_modules/minari/data_collector/data_collector/index.html index 542e1123..b7dafb25 100644 --- a/main/_modules/minari/data_collector/data_collector/index.html +++ b/main/_modules/minari/data_collector/data_collector/index.html @@ -361,12 +361,10 @@

Source code for minari.data_collector.data_collector

from __future__ import annotations import copy -import inspect import os import secrets import shutil import tempfile -import warnings from typing import Any, Callable, Dict, List, Optional, SupportsFloat, Type, Union import gymnasium as gym @@ -382,6 +380,7 @@

Source code for minari.data_collector.data_collector

) from minari.dataset.minari_dataset import MinariDataset from minari.dataset.minari_storage import MinariStorage +from minari.utils import _generate_dataset_metadata, _generate_dataset_path # H5Py supports ints up to uint64 @@ -390,17 +389,6 @@

Source code for minari.data_collector.data_collector

EpisodeBuffer = Dict[str, Any] # TODO: narrow this down -def __getattr__(name): - if name == "DataCollectorV0": - stacklevel = len(inspect.stack(0)) - warnings.warn("DataCollectorV0 is deprecated and will be removed. Use DataCollector instead.", DeprecationWarning, stacklevel=stacklevel) - return DataCollector - elif name == "__path__": - return False # see https://stackoverflow.com/a/60803436 - else: - raise ImportError(f"cannot import name '{name}' from '{__name__}' ({__file__})") - -
[docs] class DataCollector(gym.Wrapper): @@ -719,8 +707,6 @@

Source code for minari.data_collector.data_collector

Returns: MinariDataset """ - # TODO: move the import to top of the file after removing minari.create_dataset_from_collector_env() in 0.5.0 - from minari.utils import _generate_dataset_metadata, _generate_dataset_path dataset_path = _generate_dataset_path(dataset_id) metadata: Dict[str, Any] = _generate_dataset_metadata( dataset_id, @@ -737,7 +723,7 @@

Source code for minari.data_collector.data_collector

minari_version, ) - self.save_to_disk(dataset_path, metadata) + self._save_to_disk(dataset_path, metadata) # will be able to calculate dataset size only after saving the disk, so updating the dataset metadata post `save_to_disk` method @@ -746,7 +732,7 @@

Source code for minari.data_collector.data_collector

dataset.storage.update_metadata(metadata) return dataset - def save_to_disk( + def _save_to_disk( self, path: str | os.PathLike, dataset_metadata: Dict[str, Any] = {} ): """Save all in-memory buffer data and move temporary files to a permanent location in disk. @@ -755,7 +741,6 @@

Source code for minari.data_collector.data_collector

path (str): path to store the dataset, e.g.: '/home/foo/datasets/data' dataset_metadata (Dict, optional): additional metadata to add to the dataset file. Defaults to {}. """ - warnings.warn("This method is deprecated and will become private in v0.5.0.", DeprecationWarning, stacklevel=2) self._validate_buffer() self._storage.update_episodes(self._buffer) self._buffer.clear() diff --git a/main/_modules/minari/dataset/episode_data/index.html b/main/_modules/minari/dataset/episode_data/index.html index 65b93ab9..391e4c76 100644 --- a/main/_modules/minari/dataset/episode_data/index.html +++ b/main/_modules/minari/dataset/episode_data/index.html @@ -375,7 +375,7 @@

Source code for minari.dataset.episode_data

 
     id: int
     seed: Optional[int]
-    total_timesteps: int
+    total_steps: int
     observations: Any
     actions: Any
     rewards: np.ndarray
@@ -388,7 +388,7 @@ 
Source code for minari.dataset.episode_data
             "EpisodeData("
             f"id={repr(self.id)}, "
             f"seed={repr(self.seed)}, "
-            f"total_timesteps={self.total_timesteps}, "
+            f"total_steps={self.total_steps}, "
             f"observations={EpisodeData._repr_space_values(self.observations)}, "
             f"actions={EpisodeData._repr_space_values(self.actions)}, "
             f"rewards=ndarray of {len(self.rewards)} floats, "
diff --git a/main/_modules/minari/dataset/minari_dataset/index.html b/main/_modules/minari/dataset/minari_dataset/index.html
index 65523ef3..eaf75c72 100644
--- a/main/_modules/minari/dataset/minari_dataset/index.html
+++ b/main/_modules/minari/dataset/minari_dataset/index.html
@@ -627,7 +627,7 @@ 
Source code for minari.dataset.minari_dataset
else:
                 self._total_steps = sum(
                     self.storage.apply(
-                        lambda episode: episode["total_timesteps"],
+                        lambda episode: episode["total_steps"],
                         episode_indices=self.episode_indices,
                     )
                 )
diff --git a/main/_modules/minari/utils/index.html b/main/_modules/minari/utils/index.html
index 9a0b9061..8aa75324 100644
--- a/main/_modules/minari/utils/index.html
+++ b/main/_modules/minari/utils/index.html
@@ -376,7 +376,6 @@ 
Source code for minari.utils
 from packaging.specifiers import InvalidSpecifier, SpecifierSet
 from packaging.version import Version
 
-from minari import DataCollector
 from minari.dataset.minari_dataset import MinariDataset
 from minari.dataset.minari_storage import MinariStorage
 from minari.storage.datasets_root_dir import get_dataset_path
@@ -935,63 +934,6 @@ 
Source code for minari.utils
 
 
 
-def create_dataset_from_collector_env(
-    dataset_id: str,
-    collector_env: DataCollector,
-    eval_env: Optional[str | gym.Env | EnvSpec] = None,
-    algorithm_name: Optional[str] = None,
-    author: Optional[str] = None,
-    author_email: Optional[str] = None,
-    code_permalink: Optional[str] = None,
-    ref_min_score: Optional[float] = None,
-    ref_max_score: Optional[float] = None,
-    expert_policy: Optional[Callable[[ObsType], ActType]] = None,
-    num_episodes_average_score: int = 100,
-    minari_version: Optional[str] = None,
-):
-    """Create a Minari dataset using the data collected from stepping with a Gymnasium environment wrapped with a `DataCollector` Minari wrapper.
-
-    The ``dataset_id`` parameter corresponds to the name of the dataset, with the syntax as follows:
-    ``(env_name-)(dataset_name)(-v(version))`` where ``env_name`` identifies the name of the environment used to generate the dataset ``dataset_name``.
-    This ``dataset_id`` is used to load the Minari datasets with :meth:`minari.load_dataset`.
-
-    Args:
-        dataset_id (str): name id to identify Minari dataset
-        collector_env (DataCollector): Gymnasium environment used to collect the buffer data
-        buffer (list[Dict[str, Union[list, Dict]]]): list of episode dictionaries with data
-        eval_env (Optional[str|gym.Env|EnvSpec]): Gymnasium environment(gym.Env)/environment id(str)/environment spec(EnvSpec) to use for evaluation with the dataset. After loading the dataset, the environment can be recovered as follows: `MinariDataset.recover_environment(eval_env=True).
-                                                If None the `env` used to collect the buffer data should be used for evaluation.
-        algorithm_name (Optional[str], optional): name of the algorithm used to collect the data. Defaults to None.
-        author (Optional[str], optional): author that generated the dataset. Defaults to None.
-        author_email (Optional[str], optional): email of the author that generated the dataset. Defaults to None.
-        code_permalink (Optional[str], optional): link to relevant code used to generate the dataset. Defaults to None.
-        ref_min_score( Optional[float], optional): minimum reference score from the average returns of a random policy. This value is later used to normalize a score with :meth:`minari.get_normalized_score`. If default None the value will be estimated with a default random policy.
-        ref_max_score (Optional[float], optional: maximum reference score from the average returns of a hypothetical expert policy. This value is used in :meth:`minari.get_normalized_score`. Default None.
-        expert_policy (Optional[Callable[[ObsType], ActType], optional): policy to compute `ref_max_score` by averaging the returns over a number of episodes equal to  `num_episodes_average_score`.
-                                                                        `ref_max_score` and `expert_policy` can't be passed at the same time. Default to None
-        num_episodes_average_score (int): number of episodes to average over the returns to compute `ref_min_score` and `ref_max_score`. Default to 100.
-        minari_version (Optional[str], optional): Minari version specifier compatible with the dataset. If None (default) use the installed Minari version.
-
-    Returns:
-        MinariDataset
-    """
-    warnings.warn("This function is deprecated and will be removed in v0.5.0. Please use DataCollector.create_dataset() instead.", DeprecationWarning, stacklevel=2)
-    dataset = collector_env.create_dataset(
-        dataset_id=dataset_id,
-        eval_env=eval_env,
-        algorithm_name=algorithm_name,
-        author=author,
-        author_email=author_email,
-        code_permalink=code_permalink,
-        ref_min_score=ref_min_score,
-        ref_max_score=ref_max_score,
-        expert_policy=expert_policy,
-        num_episodes_average_score=num_episodes_average_score,
-        minari_version=minari_version,
-    )
-    return dataset
-
-
 

 [docs]
 def get_normalized_score(dataset: MinariDataset, returns: np.ndarray) -> np.ndarray:
@@ -1071,7 +1013,7 @@ 
Source code for minari.utils
         version += f" ({__version__} installed)"
 
     md_dict = {
-        "Total Timesteps": dataset_spec["total_steps"],
+        "Total steps": dataset_spec["total_steps"],
         "Total Episodes": dataset_spec["total_episodes"],
         "Dataset Observation Space": f"`{dataset_observation_space}`",
         "Dataset Action Space": f"`{dataset_action_space}`",
diff --git a/main/api/data_collector/index.html b/main/api/data_collector/index.html
index d003bf30..576eea16 100644
--- a/main/api/data_collector/index.html
+++ b/main/api/data_collector/index.html
@@ -460,13 +460,6 @@ 
Methods#<
 
 
 
-

-

-minari.DataCollector.close(self)#

-
Close the DataCollector.

-
Clear buffer and close temporary directory.

-

-
 

 

 minari.DataCollector.create_dataset(self, dataset_id: str, eval_env: str | gym.Env | EnvSpec | None = None, algorithm_name: str | None = None, author: str | None = None, author_email: str | None = None, code_permalink: str | None = None, ref_min_score: float | None = None, ref_max_score: float | None = None, expert_policy: Callable[[ObsType], ActType] | None = None, num_episodes_average_score: int = 100, minari_version: str | None = None)#

@@ -500,6 +493,13 @@ 
Methods#<
 

 
 
+

+

+minari.DataCollector.close(self)#

+
Close the DataCollector.

+
Clear buffer and close temporary directory.

+

+
 
 
 
@@ -579,8 +579,8 @@ 
Methods#<
 
  • Methods
 
    • 
 
diff --git a/main/api/minari_dataset/episode_data/index.html b/main/api/minari_dataset/episode_data/index.html
index e71635b8..b97bead8 100644
--- a/main/api/minari_dataset/episode_data/index.html
+++ b/main/api/minari_dataset/episode_data/index.html
@@ -373,7 +373,7 @@ 
    EpisodeData#
    
 
    
 
    
-class minari.EpisodeData(id: int, seed: int | None, total_timesteps: int, observations: Any, actions: Any, rewards: ndarray, terminations: ndarray, truncations: ndarray, infos: dict)[source]#
    
+class minari.EpisodeData(id: int, seed: int | None, total_steps: int, observations: Any, actions: Any, rewards: ndarray, terminations: ndarray, truncations: ndarray, infos: dict)[source]#
 
    Contains the datasets data for a single episode.
    
 
    This is the object returned by minari.MinariDataset.sample_episodes.
    
 
    
@@ -393,22 +393,22 @@ 
    Attributes
-
    
-EpisodeData.total_timesteps: int#
    
-
    The number of timesteps contained in this episode.
    
+
    
+EpisodeData.total_steps: int#
    
+
    The number of steps contained in this episode.
    
 
    
 
 
    
 
    
 EpisodeData.observations: Any#
    
 
    The observations of the environment. The initial and final observations are included meaning that the number
-of observations will be increased by one compared to the number of timesteps
    
+of observations will be increased by one compared to the number of steps.
    
 
    
 
 
    
 
    
 EpisodeData.actions: Any#
    
-
    The actions taken in each episode timestep.
    
+
    The actions taken in each episode step.
    
 
    
 
 
    
@@ -502,7 +502,7 @@ 
    AttributesAttributes
     
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    Behavioral cloning with PyTorch#
    
 
    We present here how to perform behavioral cloning on a Minari dataset using PyTorch.
 We will start generating the dataset of the expert policy for the CartPole-v1 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#
    
 
    For this tutorial you will need the RL Baselines3 Zoo library, which you can install with pip install rl_zoo3.
@@ -463,7 +463,7 @@ 
    Behavioral cloning with PyTorchreturn {
         "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