ppo lunarlander test

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: 286.35 +/- 16.70
+      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 0x78a256df9a20>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78a256df9ab0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78a256df9b40>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78a256df9bd0>", "_build": "<function ActorCriticPolicy._build at 0x78a256df9c60>", "forward": "<function ActorCriticPolicy.forward at 0x78a256df9cf0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x78a256df9d80>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78a256df9e10>", "_predict": "<function ActorCriticPolicy._predict at 0x78a256df9ea0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78a256df9f30>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78a256df9fc0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x78a256dfa050>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x78a256da24c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1697131747890158462, "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": -0.015808000000000044, "_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": 1500, "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": 1024, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 32, "n_epochs": 20, "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": "2.0.1+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": 286.3483258, "std_reward": 16.69774210820496, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-10-12T18:24:10.995592"}

test.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f917ea78316e9be3b6df6bf03144051da76f162cc3815dea91d4c365a10c3f95
+size 146623

test/_stable_baselines3_version

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

test/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 0x78a256df9a20>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78a256df9ab0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78a256df9b40>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78a256df9bd0>",
+        "_build": "<function ActorCriticPolicy._build at 0x78a256df9c60>",
+        "forward": "<function ActorCriticPolicy.forward at 0x78a256df9cf0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x78a256df9d80>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78a256df9e10>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x78a256df9ea0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78a256df9f30>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78a256df9fc0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x78a256dfa050>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x78a256da24c0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1697131747890158462,
+    "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": -0.015808000000000044,
+    "_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": 1500,
+    "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": 1024,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 32,
+    "n_epochs": 20,
+    "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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"
+    }
+}

test/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa838a476f1f24f8e7c754cdd24110d3b5c4539adffe6531acb5cf0b359962cf
+size 87929

test/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ac1a1ebb1167be971350ce8f1eea7566cac13138bb863b5e0596641936c7f29c
+size 43329

test/pytorch_variables.pth

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

test/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: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2