Initial commit

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: 192.13 +/- 78.31
+      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
+
+...
+```

apollo11.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9ecc3cfca115df7770e7c2d39940357bc8db0fead6b3580137f03e85739a8027
+size 147386

apollo11/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

apollo11/data

@@ -0,0 +1,96 @@
+{
+    "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 0x7f798aa18670>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f798aa18700>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f798aa18790>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f798aa18820>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f798aa188b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f798aa18940>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f798aa189d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f798aa18a60>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f798aa18af0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f798aa18b80>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f798aa18c10>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f798aa18ca0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f798aa11ec0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1048576,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1682786276318790260,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_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": -0.04857599999999995,
+    "_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": 128,
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "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
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "n_steps": 4096,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 128,
+    "n_epochs": 8,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

apollo11/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5e8b9dc7751e421e7309ffdff4c2e8785212b0be6219f954299bceda81d3bc00
+size 87929

apollo11/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:46ce0932532a08f9fc0c6c08135f575b6b57628392575fcf6860fa3321f439a0
+size 43329

apollo11/pytorch_variables.pth

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

apollo11/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.10.11
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.0+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

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 0x7f798aa18670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f798aa18700>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f798aa18790>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f798aa18820>", "_build": "<function ActorCriticPolicy._build at 0x7f798aa188b0>", "forward": "<function ActorCriticPolicy.forward at 0x7f798aa18940>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f798aa189d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f798aa18a60>", "_predict": "<function ActorCriticPolicy._predict at 0x7f798aa18af0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f798aa18b80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f798aa18c10>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f798aa18ca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f798aa11ec0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1048576, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1682786276318790260, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_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": -0.04857599999999995, "_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": 128, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "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}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "n_steps": 4096, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 128, "n_epochs": 8, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.10.11", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 192.12773611742278, "std_reward": 78.31482554186357, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-29T17:02:35.343158"}