My last LunarLander model

5-mil-ppo-lunarlander.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:81f53de35ab6a8a2bcde93570f2e2e54e7486b6e131179e11ab27587e52b9ef2
+size 147000

5-mil-ppo-lunarlander/_stable_baselines3_version

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

5-mil-ppo-lunarlander/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 0x78124bd4add0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78124bd4ae60>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78124bd4aef0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78124bd4af80>",
+        "_build": "<function ActorCriticPolicy._build at 0x78124bd4b010>",
+        "forward": "<function ActorCriticPolicy.forward at 0x78124bd4b0a0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x78124bd4b130>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78124bd4b1c0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x78124bd4b250>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78124bd4b2e0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78124bd4b370>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x78124bd4b400>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x78124bceb3c0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 5013504,
+    "_total_timesteps": 5000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1696728881290285161,
+    "learning_rate": 0.0001,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWViwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJSMBW51bXB5lIwFZHR5cGWUk5SMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLGIWUjAFDlHSUUpQu"
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.0027007999999999477,
+    "_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": 816,
+    "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": 24,
+    "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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"
+    }
+}

5-mil-ppo-lunarlander/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1d2013d52a345976612786599118eb3f0e8c40e1f6ca983fe351b11d1346bd98
+size 87929

5-mil-ppo-lunarlander/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9362df12d286ff7cc769055a7a040b8e4b03d309905ea596a5adfafd40aa1c5b
+size 43329

5-mil-ppo-lunarlander/pytorch_variables.pth

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

5-mil-ppo-lunarlander/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: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

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: 274.15 +/- 20.70
+      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 0x78124bd4add0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78124bd4ae60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78124bd4aef0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78124bd4af80>", "_build": "<function ActorCriticPolicy._build at 0x78124bd4b010>", "forward": "<function ActorCriticPolicy.forward at 0x78124bd4b0a0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x78124bd4b130>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78124bd4b1c0>", "_predict": "<function ActorCriticPolicy._predict at 0x78124bd4b250>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78124bd4b2e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78124bd4b370>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x78124bd4b400>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x78124bceb3c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 5013504, "_total_timesteps": 5000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1696728881290285161, "learning_rate": 0.0001, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWViwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYYAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJSMBW51bXB5lIwFZHR5cGWUk5SMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLGIWUjAFDlHSUUpQu"}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.0027007999999999477, "_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": 816, "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": 24, "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": "2.0.1+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": 274.15464910000003, "std_reward": 20.69735361969675, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-10-08T02:58:59.366895"}