Initial Commit
Browse files- README.md +2 -0
- a2c-BreakoutNoFrameskip-v4.zip +1 -1
- a2c-BreakoutNoFrameskip-v4/data +2 -2
- results.json +1 -1
README.md
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@@ -51,6 +51,8 @@ python -m utils.push_to_hub --algo a2c --env BreakoutNoFrameskip-v4 -f logs/ -or
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## Hyperparameters
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```python
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OrderedDict([('ent_coef', 0.01),
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('frame_stack', 4),
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('n_envs', 16),
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('n_timesteps', 10000000.0),
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## Hyperparameters
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```python
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OrderedDict([('ent_coef', 0.01),
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('env_wrapper',
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['stable_baselines3.common.atari_wrappers.AtariWrapper']),
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('frame_stack', 4),
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('n_envs', 16),
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('n_timesteps', 10000000.0),
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a2c-BreakoutNoFrameskip-v4.zip
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size 13674198
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version https://git-lfs.github.com/spec/v1
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size 13674198
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a2c-BreakoutNoFrameskip-v4/data
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":serialized:": "gAWVPgAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMFEFjdG9yQ3JpdGljQ25uUG9saWN5lJOULg==",
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"__module__": "stable_baselines3.common.policies",
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"__doc__": "\n CNN policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ",
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"__init__": "<function ActorCriticCnnPolicy.__init__ at
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"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc_data object at
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},
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"verbose": 1,
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"policy_kwargs": {
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":serialized:": "gAWVPgAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMFEFjdG9yQ3JpdGljQ25uUG9saWN5lJOULg==",
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"__module__": "stable_baselines3.common.policies",
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"__doc__": "\n CNN policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ",
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"__init__": "<function ActorCriticCnnPolicy.__init__ at 0x7f13821b60e0>",
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"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc_data object at 0x7f13822179f0>"
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},
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"verbose": 1,
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"policy_kwargs": {
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results.json
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{"mean_reward": 349.5, "std_reward": 89.73878760045736, "is_deterministic": false, "n_eval_episodes": 10, "eval_datetime": "2022-05-20T10:
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{"mean_reward": 349.5, "std_reward": 89.73878760045736, "is_deterministic": false, "n_eval_episodes": 10, "eval_datetime": "2022-05-20T10:11:15.299314"}
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