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Add precommit config yaml
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.codespell_words

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Parth
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Reacher
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reacher
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theses

.pre-commit-config.yaml

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# To use:
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#
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# pre-commit run -a
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#
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# Or:
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#
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# pre-commit install # (runs every time you commit in git)
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#
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# To update this file:
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#
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# pre-commit autoupdate
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#
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# See https://github.com/pre-commit/pre-commit
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repos:
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# Standard hooks
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v4.5.0
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hooks:
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- id: check-added-large-files
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- id: check-case-conflict
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- id: check-json
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- id: check-merge-conflict
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- id: check-symlinks
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- id: check-toml
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- id: check-yaml
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- id: debug-statements
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- id: destroyed-symlinks
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- id: detect-private-key
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- id: end-of-file-fixer
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- id: mixed-line-ending
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- id: pretty-format-json
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- id: trailing-whitespace
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- repo: https://github.com/codespell-project/codespell
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rev: v2.3.0
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hooks:
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- id: codespell
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args: ['--write-changes', '--ignore-words=.codespell_words']

CODE_OF_CONDUCT.md

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We as members, contributors, and leaders pledge to make participation in our
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community a harassment-free experience for everyone, regardless of age, body
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size, visible or invisible disability, ethnicity, sex characteristics, gender
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identity and expression, level of experience, education, socio-economic status,
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identity and expression, level of experience, education, socioeconomic status,
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nationality, personal appearance, race, religion, or sexual identity
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and orientation.
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docs/_static/img/colab.svg

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docs/common/envs.rst

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-----------------
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.. autoclass:: SimpleMultiObsEnv
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:members:
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:members:

docs/modules/a2c.rst

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env = make_vec_env("CartPole-v1", n_envs=8, vec_env_cls=SubprocVecEnv)
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model = A2C("MlpPolicy", env, device="cpu")
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model.learn(total_timesteps=25_000)
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For more information, see :ref:`Vectorized Environments <vec_env>`, `Issue #1245 <https://github.com/DLR-RM/stable-baselines3/issues/1245>`_ or the `Multiprocessing notebook <https://colab.research.google.com/github/Stable-Baselines-Team/rl-colab-notebooks/blob/sb3/multiprocessing_rl.ipynb>`_.
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stable_baselines3/common/on_policy_algorithm.py

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# Reshape in case of discrete action
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actions = actions.reshape(-1, 1)
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# Handle timeout by bootstraping with value function
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# Handle timeout by bootstrapping with value function
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# see GitHub issue #633
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for idx, done in enumerate(dones):
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if (

stable_baselines3/her/her_replay_buffer.py

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"If you are in the same episode as when the replay buffer was saved,\n"
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"you should use `truncate_last_trajectory=False` to avoid that issue."
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)
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# only consider epsiodes that are not finished
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# only consider episodes that are not finished
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for env_idx in np.where(self._current_ep_start != self.pos)[0]:
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# set done = True for last episodes
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self.dones[self.pos - 1, env_idx] = True

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