Upload PPO LunarLander-v2 trained agent

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: 250.63 +/- 40.44
+      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 0x7aa4e4e14670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7aa4e4e14700>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7aa4e4e14790>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7aa4e4e14820>", "_build": "<function ActorCriticPolicy._build at 0x7aa4e4e148b0>", "forward": "<function ActorCriticPolicy.forward at 0x7aa4e4e14940>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7aa4e4e149d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7aa4e4e14a60>", "_predict": "<function ActorCriticPolicy._predict at 0x7aa4e4e14af0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7aa4e4e14b80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7aa4e4e14c10>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7aa4e4e14ca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7aa4e4e08e40>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1701829649505474360, "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:cd99240790d53be537eb8ded10c07f564b4775c53e893308ca7d984ed4834094
+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 0x7aa4e4e14670>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7aa4e4e14700>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7aa4e4e14790>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7aa4e4e14820>",
+        "_build": "<function ActorCriticPolicy._build at 0x7aa4e4e148b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7aa4e4e14940>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7aa4e4e149d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7aa4e4e14a60>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7aa4e4e14af0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7aa4e4e14b80>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7aa4e4e14c10>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7aa4e4e14ca0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7aa4e4e08e40>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1701829649505474360,
+    "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 sha256:9fe14f8f1f409bb74145df64209d4258e6b7b7b341e515c6f0a3c3699bfff192
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:895721b981af1f42c4036d7b77f7913d805e829ca1694ea24bf2a0b22e77c55c
+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": 250.6277, "std_reward": 40.44310308364115, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-12-06T02:57:47.808733"}