Upload PPO LunarLander-v2 trained agent (Lunar200K)

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: -144.32 +/- 41.46
+      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 0x7ff6fccfc550>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff6fccfc5e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff6fccfc670>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff6fccfc700>", "_build": "<function ActorCriticPolicy._build at 0x7ff6fccfc790>", "forward": "<function ActorCriticPolicy.forward at 0x7ff6fccfc820>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff6fccfc8b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff6fccfc940>", "_predict": "<function ActorCriticPolicy._predict at 0x7ff6fccfc9d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff6fccfca60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff6fccfcaf0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff6fccfcb80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ff6fccfd840>"}, "verbose": 1, "policy_kwargs": {}, "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:": "gAWVmAEAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlIwUbnVtcHkucmFuZG9tLl9waWNrbGWUjBBfX2dlbmVyYXRvcl9jdG9ylJOUjAVQQ0c2NJRoGowUX19iaXRfZ2VuZXJhdG9yX2N0b3KUk5SGlFKUfZQojA1iaXRfZ2VuZXJhdG9ylIwFUENHNjSUjAVzdGF0ZZR9lChoJYoQ42GVprdeWAktRZZZNLWhGowDaW5jlIoQqXN4RLwzgViCGvc629qNQXWMCmhhc191aW50MzKUSwCMCHVpbnRlZ2VylEsAdWJ1Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": "Generator(PCG64)"}, "n_envs": 16, "num_timesteps": 212992, "_total_timesteps": 200000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1679965101850384000, "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": -0.0649599999999999, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 52, "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, "system_info": {"OS": "macOS-10.16-x86_64-i386-64bit Darwin Kernel Version 22.3.0: Mon Jan 30 20:38:37 PST 2023; root:xnu-8792.81.3~2/RELEASE_ARM64_T6000", "Python": "3.9.16", "Stable-Baselines3": "2.0.0a0", "PyTorch": "2.0.0", "GPU Enabled": "False", "Numpy": "1.24.2", "Gym": "0.28.1"}}

ppo-LunarLander-v2-Lunar200K.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f688c04244efa5641ec58b4fa7cc2f945e3b7cc40a941c51cd73df07b0df053d
+size 146527

ppo-LunarLander-v2-Lunar200K/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a0

ppo-LunarLander-v2-Lunar200K/data

@@ -0,0 +1,98 @@
+{
+    "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 0x7ff6fccfc550>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff6fccfc5e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff6fccfc670>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff6fccfc700>",
+        "_build": "<function ActorCriticPolicy._build at 0x7ff6fccfc790>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7ff6fccfc820>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff6fccfc8b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff6fccfc940>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7ff6fccfc9d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff6fccfca60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff6fccfcaf0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff6fccfcb80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7ff6fccfd840>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "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:": "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",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": "Generator(PCG64)"
+    },
+    "n_envs": 16,
+    "num_timesteps": 212992,
+    "_total_timesteps": 200000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1679965101850384000,
+    "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": -0.0649599999999999,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 52,
+    "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
+}

ppo-LunarLander-v2-Lunar200K/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:72d4257b64de1ddfe7b57f8155404be751279c3a68a8518ade1c28011f1fc2a1
+size 87545

ppo-LunarLander-v2-Lunar200K/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9cb8a7ba4a190ed4844347ec1bf55b75d863f3bb448a26a4176ffe9b3ba73d97
+size 43201

ppo-LunarLander-v2-Lunar200K/pytorch_variables.pth

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

ppo-LunarLander-v2-Lunar200K/system_info.txt

@@ -0,0 +1,7 @@
+- OS: macOS-10.16-x86_64-i386-64bit Darwin Kernel Version 22.3.0: Mon Jan 30 20:38:37 PST 2023; root:xnu-8792.81.3~2/RELEASE_ARM64_T6000
+- Python: 3.9.16
+- Stable-Baselines3: 2.0.0a0
+- PyTorch: 2.0.0
+- GPU Enabled: False
+- Numpy: 1.24.2
+- Gym: 0.28.1

results.json

@@ -0,0 +1 @@
+{"mean_reward": -144.3226302, "std_reward": 41.46039623599709, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-27T21:02:46.098787"}