Upload PPO LunarLander-v2 trained agent in hugging face

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: -128.37 +/- 42.05
+      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 0x7b2ad1932cb0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b2ad1932d40>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b2ad1932dd0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b2ad1932e60>", "_build": "<function ActorCriticPolicy._build at 0x7b2ad1932ef0>", "forward": "<function ActorCriticPolicy.forward at 0x7b2ad1932f80>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b2ad1933010>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b2ad19330a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7b2ad1933130>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b2ad19331c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b2ad1933250>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b2ad19332e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7b2ad18e1680>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 100352, "_total_timesteps": 100000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1711647447394956805, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAObqXr1/iQM/8heBvnkRo77lGco9HX9hvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.0035199999999999676, "_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": 392, "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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "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-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.2.1+cu121", "GPU Enabled": "False", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "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:abe8fcc667825b46cba7e72fdb66c0a66020a025d179296545fa7178fc0ee616
+size 146855

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 0x7b2ad1932cb0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b2ad1932d40>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b2ad1932dd0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b2ad1932e60>",
+        "_build": "<function ActorCriticPolicy._build at 0x7b2ad1932ef0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7b2ad1932f80>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b2ad1933010>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b2ad19330a0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7b2ad1933130>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b2ad19331c0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b2ad1933250>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b2ad19332e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7b2ad18e1680>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 100352,
+    "_total_timesteps": 100000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1711647447394956805,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAObqXr1/iQM/8heBvnkRo77lGco9HX9hvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.0035199999999999676,
+    "_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": 392,
+    "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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "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:c51551ef2d32f3d4db9b54849f22625b98b1f8a8f61d35818c0ed640727fd067
+size 87978

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7308b6b6d96b98073ae2d6b21d3e526373da3c7cff57fc24e573a9a3bdce5d48
+size 43634

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-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.2.1+cu121
+- GPU Enabled: False
+- Numpy: 1.25.2
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": -128.3736844, "std_reward": 42.05082454969806, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-03-28T17:44:07.843493"}