fix commit

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,36 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 284.67 +/- 17.68
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+---
+
+# **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:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 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    ", "__init__": "<function ActorCriticPolicy.__init__ at 0x7f59030a4200>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f59030a4290>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f59030a4320>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f59030a43b0>", "_build": "<function ActorCriticPolicy._build at 0x7f59030a4440>", "forward": "<function ActorCriticPolicy.forward at 0x7f59030a44d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f59030a4560>", "_predict": "<function ActorCriticPolicy._predict at 0x7f59030a45f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f59030a4680>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f59030a4710>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f59030a47a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f59030f07b0>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null, "_shape": [8]}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gASViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwFZHR5cGWUjAVudW1weZSMBWR0eXBllJOUjAJpOJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRijApfbnBfcmFuZG9tlE6MBl9zaGFwZZQpdWIu", "n": 4, "dtype": "int64", "_np_random": null, "_shape": []}, "n_envs": 32, "num_timesteps": 5013504, "_total_timesteps": 5000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1654457988.2378478, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": null, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gASVqAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSyCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlHSUYi4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.0027007999999999477, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 612, "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, "system_info": {"OS": "Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu113", "GPU Enabled": "False", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c9537fca74e790b226089361d7aab50eb7a60f7b22592a1071703b4cffb7bd26
+size 142781

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.5.0

ppo-LunarLander-v2/data

@@ -0,0 +1,91 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__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 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    ",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f59030a4200>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f59030a4290>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f59030a4320>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f59030a43b0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f59030a4440>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f59030a44d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f59030a4560>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f59030a45f0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f59030a4680>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f59030a4710>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f59030a47a0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f59030f07b0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "gASVwwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMA2xvd5SMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlGgGjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwiFlGgKiUMgAACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP+UdJRijARoaWdolGgQaBJLAIWUaBSHlFKUKEsBSwiFlGgKiUMgAACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH+UdJRijA1ib3VuZGVkX2JlbG93lGgQaBJLAIWUaBSHlFKUKEsBSwiFlGgHjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiiUMIAAAAAAAAAACUdJRijA1ib3VuZGVkX2Fib3ZllGgQaBJLAIWUaBSHlFKUKEsBSwiFlGgoiUMIAAAAAAAAAACUdJRijApfbnBfcmFuZG9tlE6MBl9zaGFwZZRLCIWUdWIu",
+        "dtype": "float32",
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False]",
+        "_np_random": null,
+        "_shape": [
+            8
+        ]
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
+        ":serialized:": "gASViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwFZHR5cGWUjAVudW1weZSMBWR0eXBllJOUjAJpOJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRijApfbnBfcmFuZG9tlE6MBl9zaGFwZZQpdWIu",
+        "n": 4,
+        "dtype": "int64",
+        "_np_random": null,
+        "_shape": []
+    },
+    "n_envs": 32,
+    "num_timesteps": 5013504,
+    "_total_timesteps": 5000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1654457988.2378478,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": null,
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gASVqAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSyCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlHSUYi4="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.0027007999999999477,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 612,
+    "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
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ca116a64d06201a151249525359d7b73b57d17ea3431e245d33745ded320f119
+size 84637

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6e3fc299ee79be47db024a647350afd616200714cf0e306e878f87b9427009b4
+size 43073

ppo-LunarLander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
+Python: 3.7.13
+Stable-Baselines3: 1.5.0
+PyTorch: 1.11.0+cu113
+GPU Enabled: False
+Numpy: 1.21.6
+Gym: 0.21.0

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:64bdb19463ad2ae89d69b68107adea5ea67c05d0b9ff87d81bca94e2762e8a2e
+size 206436

results.json

@@ -0,0 +1 @@
+{"mean_reward": 284.6689281838635, "std_reward": 17.67616209263117, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-06-05T21:33:05.545037"}