init

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: 239.44 +/- 15.88
+      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
+
+...
+```

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 0x7f8e528176d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8e52817760>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8e528177f0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8e52817880>", "_build": "<function ActorCriticPolicy._build at 0x7f8e52817910>", "forward": "<function ActorCriticPolicy.forward at 0x7f8e528179a0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f8e52817a30>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8e52817ac0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f8e52817b50>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8e52817be0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8e52817c70>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8e52817d00>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f8e5ab95d00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1700706980602356018, "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": 248, "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": 0.999, "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-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": "2.1.0+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "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 sha256:2a224b2ed84e5c50922bef730532cf4e237dcd33a73c7fea064ffce266a1ee6f
+size 148054

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 0x7f8e528176d0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8e52817760>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8e528177f0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8e52817880>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f8e52817910>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f8e528179a0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f8e52817a30>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8e52817ac0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f8e52817b50>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8e52817be0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8e52817c70>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8e52817d00>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f8e5ab95d00>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1700706980602356018,
+    "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": 248,
+    "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": 0.999,
+    "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 sha256:44337ee4eaf48a468ae56eff32b21ed484b7b71d2cab0222ddc977fcf53f8da0
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ad533507fdeeba4036b429ad26499befbe13e7574ab07dc90524c219306ebdc4
+size 43762

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-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: 2.1.0+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": 239.43955139999997, "std_reward": 15.881276004773055, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-23T03:06:47.814979"}