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‎.github/workflows/continuous_integration.yml

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@@ -4,7 +4,7 @@ name: Continuous Integration
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on:
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push:
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branches: [ dev, master ]
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branches: [ dev, dev-v1, master ]
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jobs:
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build:

‎MANIFEST.in

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include requirements.txt
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include setup.py
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prune tests
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prune examples
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prune .github

‎docs/conf.py

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#
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# This is also used if you do content translation via gettext catalogs.
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# Usually you set "language" from the command line for these cases.
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language = None
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language = 'en'
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# List of patterns, relative to source directory, that match files and
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# directories to ignore when looking for source files.

‎docs/index.rst

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@@ -53,7 +53,7 @@ Then, an epsilon-greedy **policy** with:
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::
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from mushroom_rl.policy import EpsGreedy
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from mushroom_rl.utils.parameters import Parameter
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from mushroom_rl.rl_utils.parameters import Parameter
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epsilon = Parameter(value=1.)
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policy = EpsGreedy(epsilon=epsilon)

‎docs/source/features/mushroom_rl.features.tensors.rst

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Tensors
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=======
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Gaussian tensor
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---------------
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.. automodule:: mushroom_rl.features.tensors.constant_tensor
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:members:
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:private-members:
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:show-inheritance:
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.. automodule:: mushroom_rl.features.tensors.basis_tensor
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:members:
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:private-members:
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:show-inheritance:
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.. automodule:: mushroom_rl.features.tensors.gaussian_tensor
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.. automodule:: mushroom_rl.features.tensors.random_fourier_tensor
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:members:
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:private-members:
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:show-inheritance:

‎docs/source/mushroom_rl.approximators.rst

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:show-inheritance:
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Approximator
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Approximators
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-------------
2020

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Tabular
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~~~~~~~
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.. automodule:: mushroom_rl.approximators.table
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:members:
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:private-members:
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:inherited-members:
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:show-inheritance:
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Linear
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~~~~~~
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‎docs/source/mushroom_rl.rl_utils.rst

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Reinforcement Learning utils
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============================
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Eligibility trace
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-----------------
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.. automodule:: mushroom_rl.rl_utils.eligibility_trace
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:members:
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:private-members:
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:inherited-members:
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:undoc-members:
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:show-inheritance:
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Optimizers
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----------
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.. automodule:: mushroom_rl.rl_utils.optimizers
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:members:
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:private-members:
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:inherited-members:
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:undoc-members:
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:show-inheritance:
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Parameters
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----------
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.. automodule:: mushroom_rl.rl_utils.parameters
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:members:
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:private-members:
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:inherited-members:
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:show-inheritance:
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Preprocessors
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-------------
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.. automodule:: mushroom_rl.rl_utils.preprocessors
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:members:
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:private-members:
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:inherited-members:
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:show-inheritance:
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Replay memory
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-------------
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.. automodule:: mushroom_rl.rl_utils.replay_memory
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:members:
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:private-members:
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:inherited-members:
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:show-inheritance:
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Running Statistics
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------------------
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.. automodule:: mushroom_rl.rl_utils.running_stats
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:members:
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:private-members:
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:inherited-members:
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:show-inheritance:
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Spaces
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------
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.. automodule:: mushroom_rl.rl_utils.spaces
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:members:
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:show-inheritance:
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Value Functions
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---------------
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.. automodule:: mushroom_rl.rl_utils.value_functions
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:members:
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:private-members:
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:inherited-members:
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:show-inheritance:
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Variance parameters
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-------------------
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.. automodule:: mushroom_rl.rl_utils.variance_parameters
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:members:
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:private-members:
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:inherited-members:
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:show-inheritance:

‎docs/source/mushroom_rl.utils.rst

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:members:
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:show-inheritance:
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Callbacks
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---------
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.. automodule:: mushroom_rl.utils.callbacks
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:members:
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:show-inheritance:
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Dataset
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-------
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.. automodule:: mushroom_rl.utils.dataset
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:members:
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:show-inheritance:
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Eligibility trace
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-----------------
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.. automodule:: mushroom_rl.utils.eligibility_trace
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:members:
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:private-members:
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:inherited-members:
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:undoc-members:
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:show-inheritance:
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Features
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--------
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:inherited-members:
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:show-inheritance:
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Folder
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------
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.. automodule:: mushroom_rl.utils.folder
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:members:
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:private-members:
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:inherited-members:
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:show-inheritance:
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Frames
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:inherited-members:
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:show-inheritance:
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Parameters
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----------
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.. automodule:: mushroom_rl.utils.parameters
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:members:
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:private-members:
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:inherited-members:
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:show-inheritance:
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Plots
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-----
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.. automodule:: mushroom_rl.utils.plots
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.. automodule:: mushroom_rl.utils.plot
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:members:
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:private-members:
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:inherited-members:
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:show-inheritance:
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Replay memory
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-------------
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.. automodule:: mushroom_rl.utils.replay_memory
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:members:
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:private-members:
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:inherited-members:
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:show-inheritance:
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Spaces
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Record
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------
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.. automodule:: mushroom_rl.utils.spaces
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:members:
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:show-inheritance:
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Table
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-----
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.. automodule:: mushroom_rl.utils.table
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.. automodule:: mushroom_rl.utils.record
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:members:
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:private-members:
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:inherited-members:
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:show-inheritance:
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Torch
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-----
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:inherited-members:
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:show-inheritance:
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Value Functions
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---------------
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.. automodule:: mushroom_rl.utils.value_functions
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:members:
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:private-members:
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:inherited-members:
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:show-inheritance:
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Variance parameters
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-------------------
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.. automodule:: mushroom_rl.utils.variance_parameters
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:members:
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:private-members:
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:inherited-members:
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:show-inheritance:
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Viewer
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------

‎docs/source/tutorials/code/advanced_experiment.py

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from mushroom_rl.features.tiles import Tiles
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from mushroom_rl.policy import EpsGreedy
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from mushroom_rl.utils.callbacks import CollectDataset
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from mushroom_rl.utils.parameters import Parameter
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from mushroom_rl.rl_utils.parameters import Parameter
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from mushroom_rl.environments import Gym
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# MDP

‎docs/source/tutorials/code/approximator.py

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from mushroom_rl.features.tiles import Tiles
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from mushroom_rl.policy import EpsGreedy
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from mushroom_rl.utils.callbacks import CollectDataset
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from mushroom_rl.rl_utils.parameters import Parameter
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# MDP

‎docs/source/tutorials/code/ddpg.py

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from mushroom_rl.core import Core
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from mushroom_rl.environments.dm_control_env import DMControl
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from mushroom_rl.policy import OrnsteinUhlenbeckPolicy
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from mushroom_rl.utils.dataset import compute_J
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class CriticNetwork(nn.Module):
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119118
n_steps_test = 2000
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121120
dataset = core.evaluate(n_steps=n_steps_test, render=False)
122-
J = compute_J(dataset, gamma_eval)
121+
J = dataset.discounted_return
123122
print('Epoch: 0')
124123
print('J: ', np.mean(J))
125124

126125
for n in range(n_epochs):
127126
print('Epoch: ', n+1)
128127
core.learn(n_steps=n_steps, n_steps_per_fit=1)
129128
dataset = core.evaluate(n_steps=n_steps_test, render=False)
130-
J = compute_J(dataset, gamma_eval)
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J = dataset.discounted_return
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print('J: ', np.mean(J))

‎docs/source/tutorials/code/dqn.py

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import numpy as np
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import torch
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import torch.nn as nn
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import torch.optim as optim
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from mushroom_rl.core import Core
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from mushroom_rl.environments import Atari
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from mushroom_rl.utils.dataset import compute_metrics
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from mushroom_rl.rl_utils.parameters import LinearParameter, Parameter
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class Network(nn.Module):
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def get_stats(dataset):
64-
score = compute_metrics(dataset)
62+
score = dataset.compute_metrics()
6563
print(('min_reward: %f, max_reward: %f, mean_reward: %f,'
6664
' median_reward: %f, games_completed: %d' % score))
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‎docs/source/tutorials/code/logger.py

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from mushroom_rl.environments.generators import generate_simple_chain
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46-
from mushroom_rl.utils.parameters import Parameter
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from mushroom_rl.utils.dataset import compute_J
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from mushroom_rl.rl_utils.parameters import Parameter
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from tqdm import trange
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from time import sleep
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import numpy as np
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6362
logger.strong_line()
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dataset = core.evaluate(n_steps=100)
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J = np.mean(compute_J(dataset, mdp.info.gamma)) # Discounted returns
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R = np.mean(compute_J(dataset)) # Undiscounted returns
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J = np.mean(dataset.discounted_return)
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R = np.mean(dataset.undiscounted_return) # Undiscounted returns
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logger.epoch_info(0, J=J, R=R, any_label='any value')
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sleep(0.5)
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sleep(0.5)
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J = np.mean(compute_J(dataset, mdp.info.gamma)) # Discounted returns
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R = np.mean(compute_J(dataset)) # Undiscounted returns
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J = np.mean(dataset.discounted_return) # Discounted returns
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R = np.mean(dataset.undiscounted_return) # Undiscounted returns
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# Here logging epoch results to the console
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logger.epoch_info(i+1, J=J, R=R)

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