My first PPO model for lunar lander

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: 254.62 +/- 14.51
+      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 0x7b31dfe37880>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b31dfe37910>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b31dfe379a0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b31dfe37a30>", "_build": "<function ActorCriticPolicy._build at 0x7b31dfe37ac0>", "forward": "<function ActorCriticPolicy.forward at 0x7b31dfe37b50>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b31dfe37be0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b31dfe37c70>", "_predict": "<function ActorCriticPolicy._predict at 0x7b31dfe37d00>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b31dfe37d90>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b31dfe37e20>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b31dfe37eb0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7b3188bedf80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1507328, "_total_timesteps": 1500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1690550314224248282, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAADNYfr32fGQ7Wk0xPSdxTL55M2M94jLVvAAAAAAAAIA/8zElvgWxzz7Tn1O57Ma2vhN6jb2SX2O9AAAAAAAAAAAz5uc8EnKLP41Zbz2WFdC+hNJfPTC0QT0AAAAAAAAAAKLApL4PH3I/ub2cvhptD7+O9N6+9rDZPAAAAAAAAAAATb0evdjnlT6jHdm6Nz7CvgwU4Dt+nJY6AAAAAAAAAADNTz69T6YZvFI6Q7x/Q948wcmNPcACub0AAIA/AACAP01EU73hPu66s17qu1lqTzxssDC7ep89vQAAgD8AAIA/Mx3jPGJoJT/7Fru93nbXvsdrnDtwSt68AAAAAAAAAAAAKPk9ppy+P6ZrCD8pFtK9tpQqPnlynz4AAAAAAAAAALO+wz1J80M+sQCOvjzkWL7fLWK9+4/nvQAAAAAAAAAAplAqPnx0zz5IHmq+XNuMvqiuvDzOVWW9AAAAAAAAAAAQJZs+o+KMP3FbFT6Uj/2+uDmfPjBVAr4AAAAAAAAAAGZmfTtckkm89twZvLrkqzzN2qe9zYiLPQAAgD8AAIA/egJOvteqWz8yqoK+bUD2vgaAfb6ah7u8AAAAAAAAAACAqU++v9v0PksROD35S5S+vqC3veJlRr0AAAAAAAAAAADQITzG7as/XEMXPrUm7r6/7Wc8qt1fPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_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": -0.004885333333333408, "_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": 376, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "gAWVcAIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoB4wCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoECiWCAAAAAAAAAABAQEBAQEBAZRoFEsIhZRoGHSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBAoliAAAAAAAAAAAAC0wgAAtMIAAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaApLCIWUaBh0lFKUjARoaWdolGgQKJYgAAAAAAAAAAAAtEIAALRCAACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgKSwiFlGgYdJRSlIwIbG93X3JlcHKUjFtbLTkwLiAgICAgICAgLTkwLiAgICAgICAgIC01LiAgICAgICAgIC01LiAgICAgICAgIC0zLjE0MTU5MjcgIC01LgogIC0wLiAgICAgICAgIC0wLiAgICAgICBdlIwJaGlnaF9yZXBylIxTWzkwLiAgICAgICAgOTAuICAgICAgICAgNS4gICAgICAgICA1LiAgICAgICAgIDMuMTQxNTkyNyAgNS4KICAxLiAgICAgICAgIDEuICAgICAgIF2UjApfbnBfcmFuZG9tlE51Yi4=", "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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

lunarlander_v0.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f70fac6b202c6b2d5c658274360aaea8656e5696dc53730259cf6321f9127fa0
+size 146705

lunarlander_v0/_stable_baselines3_version

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

lunarlander_v0/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 0x7b31dfe37880>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b31dfe37910>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b31dfe379a0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b31dfe37a30>",
+        "_build": "<function ActorCriticPolicy._build at 0x7b31dfe37ac0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7b31dfe37b50>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b31dfe37be0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b31dfe37c70>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7b31dfe37d00>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b31dfe37d90>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b31dfe37e20>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b31dfe37eb0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7b3188bedf80>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1507328,
+    "_total_timesteps": 1500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1690550314224248282,
+    "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": -0.004885333333333408,
+    "_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": 376,
+    "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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"
+    }
+}

lunarlander_v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5fcd26a0bd318000e2e890ee7784546f2ebeed21239feb2029a9c054ebe56844
+size 87929

lunarlander_v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a90e43fc6e86330ffdd313fad2f036caf281533db3defe67953f3b9d2b365a7c
+size 43329

lunarlander_v0/pytorch_variables.pth

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

lunarlander_v0/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.6
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 254.61686559999998, "std_reward": 14.508322169441033, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-28T13:50:09.839107"}