Test push from unit1 notebook

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO with MLP policy for LunarLander-v2 environment
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 269.21 +/- 16.42
+      name: mean_reward
+      verified: false
+---
+
+# **PPO with MLP policy for LunarLander-v2 environment** Agent playing **LunarLander-v2**
+This is a trained model of a **PPO with MLP policy for LunarLander-v2 environment** 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 0x7cb82ae2dda0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7cb82ae2de40>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7cb82ae2dee0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7cb82ae2df80>", "_build": "<function ActorCriticPolicy._build at 0x7cb82ae2e020>", "forward": "<function ActorCriticPolicy.forward at 0x7cb82ae2e0c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7cb82ae2e160>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7cb82ae2e200>", "_predict": "<function ActorCriticPolicy._predict at 0x7cb82ae2e2a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7cb82ae2e340>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7cb82ae2e3e0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7cb82ae2e480>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7cb82af2d140>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1748263894840172972, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlgAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAN5va96rF3P/62/r35rJ2+KmgJvpvsZD0AAAAAAAAAAJSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJLAUsIhpSMAUOUdJRSlC4="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksBhZSMAUOUdJRSlC4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00044800000000000395, "_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": 3908, "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:": "gAWV3AAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFm51bXB5Ll9jb3JlLm11bHRpYXJyYXmUjAZzY2FsYXKUk5SMBW51bXB5lIwFZHR5cGWUk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJDCAQAAAAAAAAAlIaUUpSMBXN0YXJ0lGgIaA5DCAAAAAAAAAAAlIaUUpSMBl9zaGFwZZQpjAVkdHlwZZRoDowKX25wX3JhbmRvbZROdWIu", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 1, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "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-6.1.123+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Mar 30 16:01:29 UTC 2025", "Python": "3.11.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.6.0+cu124", "GPU Enabled": "True", "Numpy": "2.0.2", "Cloudpickle": "3.1.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:95b9fb9efb559dcefab057791323d37ee3a5e5fdc0ff5b3952da4ae4d70ce90b
+size 147472

ppo-LunarLander-v2/_stable_baselines3_version

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

ppo-LunarLander-v2/data

@@ -0,0 +1,99 @@
+{
+    "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 0x7cb82ae2dda0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7cb82ae2de40>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7cb82ae2dee0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7cb82ae2df80>",
+        "_build": "<function ActorCriticPolicy._build at 0x7cb82ae2e020>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7cb82ae2e0c0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7cb82ae2e160>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7cb82ae2e200>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7cb82ae2e2a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7cb82ae2e340>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7cb82ae2e3e0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7cb82ae2e480>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7cb82af2d140>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1000448,
+    "_total_timesteps": 1000000.0,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1748263894840172972,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVlgAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAN5va96rF3P/62/r35rJ2+KmgJvpvsZD0AAAAAAAAAAJSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJLAUsIhpSMAUOUdJRSlC4="
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdQAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksBhZSMAUOUdJRSlC4="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.00044800000000000395,
+    "_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": 3908,
+    "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:": "gAWV3AAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFm51bXB5Ll9jb3JlLm11bHRpYXJyYXmUjAZzY2FsYXKUk5SMBW51bXB5lIwFZHR5cGWUk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJDCAQAAAAAAAAAlIaUUpSMBXN0YXJ0lGgIaA5DCAAAAAAAAAAAlIaUUpSMBl9zaGFwZZQpjAVkdHlwZZRoDowKX25wX3JhbmRvbZROdWIu",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 1,
+    "n_steps": 1024,
+    "gamma": 0.999,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 4,
+    "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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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:750549f7fbff1850e9190ae45e32288a679a9a68cf4fa62a244478d436f760b5
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ddac1b1c90bcea0798297536b8dc47c7bbc45e78d4211554bc5737ecdc0d654f
+size 43762

ppo-LunarLander-v2/pytorch_variables.pth

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

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-6.1.123+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Mar 30 16:01:29 UTC 2025
+- Python: 3.11.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.6.0+cu124
+- GPU Enabled: True
+- Numpy: 2.0.2
+- Cloudpickle: 3.1.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:35669e818916dedc714810223e9a1a7fad24ffca468cf1b26d0c08319d17ec1d
+size 142512

results.json

@@ -0,0 +1 @@
+{"mean_reward": 269.2095712, "std_reward": 16.419582063931262, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-05-26T13:29:55.508536"}