Recovery

Gagarin.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e547c35046524fc64330f2c36bdba61631528959d349a997a59683ecc0e011b2
+size 52966

Gagarin/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

Gagarin/data

@@ -0,0 +1,115 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    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 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 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    ",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7aecddf25240>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7aecddf252d0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7aecddf25360>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7aecddf253f0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7aecddf25480>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7aecddf25510>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7aecddf255a0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7aecddf25630>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7aecddf256c0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7aecddf25750>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7aecddf257e0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7aecddf25870>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7aecddf28800>"
+    },
+    "verbose": 0,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVaQAAAAAAAAB9lCiMDWFjdGl2YXRpb25fZm6UjBt0b3JjaC5ubi5tb2R1bGVzLmFjdGl2YXRpb26UjARSZUxVlJOUjAhuZXRfYXJjaJR9lCiMAnBplF2UKEsQSxBLEGWMAnZmlF2UKEsQSxBLEGV1dS4=",
+        "activation_fn": "<class 'torch.nn.modules.activation.ReLU'>",
+        "net_arch": {
+            "pi": [
+                16,
+                16,
+                16
+            ],
+            "vf": [
+                16,
+                16,
+                16
+            ]
+        }
+    },
+    "num_timesteps": 5013504,
+    "_total_timesteps": 5000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1699430251918846189,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.0027007999999999477,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 1530,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_shape": [
+            8
+        ],
+        "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "n_steps": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

Gagarin/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5bc37884b65228aefbdefa32ebfa8e7c7b33ce97cb50d984e0779a975443d8a5
+size 24902

Gagarin/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:865fbc7d50ecbff8a65e8c3c123ff1ea015ac814931ba14089866c13e38e4aa6
+size 11750

Gagarin/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

Gagarin/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.1.0+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 288.87 +/- 17.85
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **LunarLander-v2**
+This is a trained model of a **PPO** agent playing **LunarLander-v2**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    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 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 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    ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7aecddf25240>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7aecddf252d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7aecddf25360>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7aecddf253f0>", "_build": "<function ActorCriticPolicy._build at 0x7aecddf25480>", "forward": "<function ActorCriticPolicy.forward at 0x7aecddf25510>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7aecddf255a0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7aecddf25630>", "_predict": "<function ActorCriticPolicy._predict at 0x7aecddf256c0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7aecddf25750>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7aecddf257e0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7aecddf25870>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7aecddf28800>"}, "verbose": 0, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVaQAAAAAAAAB9lCiMDWFjdGl2YXRpb25fZm6UjBt0b3JjaC5ubi5tb2R1bGVzLmFjdGl2YXRpb26UjARSZUxVlJOUjAhuZXRfYXJjaJR9lCiMAnBplF2UKEsQSxBLEGWMAnZmlF2UKEsQSxBLEGV1dS4=", "activation_fn": "<class 'torch.nn.modules.activation.ReLU'>", "net_arch": {"pi": [16, 16, 16], "vf": [16, 16, 16]}}, "num_timesteps": 5013504, "_total_timesteps": 5000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1699430251918846189, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.0027007999999999477, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 1530, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "gAWVcAIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoB4wCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoECiWCAAAAAAAAAABAQEBAQEBAZRoFEsIhZRoGHSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBAoliAAAAAAAAAAAAC0wgAAtMIAAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaApLCIWUaBh0lFKUjARoaWdolGgQKJYgAAAAAAAAAAAAtEIAALRCAACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgKSwiFlGgYdJRSlIwIbG93X3JlcHKUjFtbLTkwLiAgICAgICAgLTkwLiAgICAgICAgIC01LiAgICAgICAgIC01LiAgICAgICAgIC0zLjE0MTU5MjcgIC01LgogIC0wLiAgICAgICAgIC0wLiAgICAgICBdlIwJaGlnaF9yZXBylIxTWzkwLiAgICAgICAgOTAuICAgICAgICAgNS4gICAgICAgICA1LiAgICAgICAgIDMuMTQxNTkyNyAgNS4KICAxLiAgICAgICAgIDEuICAgICAgIF2UjApfbnBfcmFuZG9tlE51Yi4=", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 288.8737752, "std_reward": 17.847514688393968, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-08T10:22:42.851796"}