Upload 6 files
Browse files- _stable_baselines3_version +1 -0
- data +88 -0
- policy.optimizer.pth +3 -0
- policy.pth +3 -0
- pytorch_variables.pth +3 -0
- system_info.txt +7 -0
_stable_baselines3_version
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{
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"policy_class": {
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":type:": "<class 'abc.ABCMeta'>",
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"__module__": "stable_baselines3.common.policies",
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"__doc__": "\n MultiInputActorClass 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 (Tuple)\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 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: Uses the CombinedExtractor\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 MultiInputActorCriticPolicy.__init__ at 0x0000023EB86E0A60>",
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"__abstractmethods__": "frozenset()",
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"_abc_impl": "<_abc._abc_data object at 0x0000023EB86DA3C0>"
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},
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"verbose": 1,
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"policy_kwargs": {
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":type:": "<class 'dict'>",
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"optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
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"optimizer_kwargs": {
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"alpha": 0.99,
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"eps": 1e-05,
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"weight_decay": 0
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}
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},
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"observation_space": {
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":type:": "<class 'gym.spaces.dict.Dict'>",
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"spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])",
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"_shape": null,
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"dtype": null,
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"_np_random": null
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},
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"action_space": {
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":type:": "<class 'gym.spaces.box.Box'>",
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},
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"num_timesteps": 1000,
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"learning_rate": 0.0007,
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":type:": "<class 'function'>",
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},
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"_last_obs": {
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"achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]]",
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"observation": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]]"
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},
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"_last_episode_starts": {
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":type:": "<class 'numpy.ndarray'>",
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},
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"_last_original_obs": null,
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"use_sde": false,
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"sde_sample_freq": -1,
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"ep_info_buffer": {
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":type:": "<class 'collections.deque'>",
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},
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"ep_success_buffer": {
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":type:": "<class 'collections.deque'>",
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":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
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},
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"_n_updates": 20000,
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"n_steps": 5,
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"gamma": 0.99,
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"gae_lambda": 1.0,
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"ent_coef": 0.0,
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"vf_coef": 0.5,
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"max_grad_norm": 0.5,
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"normalize_advantage": false
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}
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policy.optimizer.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:11a038daeed49dd488fba50aab25d28ddb736a437c7d77f94f538127961fbb5a
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size 45758
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policy.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:cfc1143ff846fe6256b0ea148764a575d86194b55289be6d069066ed022ea95e
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size 47038
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pytorch_variables.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
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size 431
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system_info.txt
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- OS: Windows-10-10.0.19045-SP0 10.0.19045
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- Python: 3.10.10
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- Stable-Baselines3: 1.7.0
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- PyTorch: 1.13.1+cu116
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- GPU Enabled: True
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- Numpy: 1.24.0
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- Gym: 0.21.0
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