Upload PPO LunarLander-v2 model

README.md

@@ -1,37 +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: 190.86 +/- 105.88
-      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
-
-...
-```
+---
+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: 2.26 +/- 47.99
+      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

@@ -1 +1 @@
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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 0x7eeb01891440>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7eeb018914e0>", "reset_noise": 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ppo-LunarLander-v2.zip

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ppo-LunarLander-v2/data

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         "__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 0x7eeb01891440>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7eeb018914e0>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7eeb01891580>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7eeb01891620>",
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-        "forward": "<function ActorCriticPolicy.forward at 0x7eeb01891760>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7eeb01891800>",
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-        "_predict": "<function ActorCriticPolicy._predict at 0x7eeb01891940>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7eeb018919e0>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7eeb01891a80>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7eeb01891b20>",
+        "__firstlineno__": 390,
+        "__doc__": "\nPolicy class for actor-critic algorithms (has both policy and value prediction).\nUsed 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 0x00000224A62EA700>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x00000224A62EA7A0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x00000224A62EA840>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x00000224A62EA8E0>",
+        "_build": "<function ActorCriticPolicy._build at 0x00000224A62EA980>",
+        "forward": "<function ActorCriticPolicy.forward at 0x00000224A62EAA20>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x00000224A62EAAC0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x00000224A62EAB60>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x00000224A62EAC00>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x00000224A62EACA0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x00000224A62EAD40>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x00000224A62EADE0>",
+        "__static_attributes__": [
+            "action_dist",
+            "action_net",
+            "activation_fn",
+            "dist_kwargs",
+            "features_dim",
+            "features_extractor",
+            "log_std",
+            "log_std_init",
+            "mlp_extractor",
+            "net_arch",
+            "optimizer",
+            "ortho_init",
+            "pi_features_extractor",
+            "share_features_extractor",
+            "use_sde",
+            "value_net",
+            "vf_features_extractor"
+        ],
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7eeb01809500>"
+        "_abc_impl": "<_abc._abc_data object at 0x00000224A617BA00>"
     },
     "verbose": 1,
     "policy_kwargs": {},
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-    "_total_timesteps": 1000000,
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+    "_total_timesteps": 100000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1761033246611841320,
+    "start_time": 1761064791257128700,
     "learning_rate": 0.0003,
     "tensorboard_log": "./ppo_lunarlander_tensorboard/",
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
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     "_last_episode_starts": {
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     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 4890,
+    "_n_updates": 490,
     "observation_space": {
         ":type:": "<class 'gymnasium.spaces.box.Box'>",
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@@ -87,13 +107,13 @@
     "n_epochs": 10,
     "clip_range": {
         ":type:": "<class 'function'>",
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     },
     "clip_range_vf": null,
     "normalize_advantage": true,
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ppo-LunarLander-v2/system_info.txt

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-- OS: Linux-6.6.105+-x86_64-with-glibc2.35 # 1 SMP Thu Oct  2 10:42:05 UTC 2025
-- Python: 3.12.12
+- OS: Windows-10-10.0.19045-SP0 10.0.19045
+- Python: 3.13.7
 - Stable-Baselines3: 2.0.0a5
-- PyTorch: 2.8.0+cu126
-- GPU Enabled: True
-- Numpy: 2.0.2
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+- GPU Enabled: False
+- Numpy: 2.3.4
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results.json

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