2nd Upload PPO LunarLander-v2 trained agent, possibly

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: 272.74 +/- 20.10
+      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
+
+...
+```

ThrustLander_R2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:202e2aa11e1858ba08b27c74d9bfc7705184d0c4383b22236974a163d5a0acd8
+size 147328

ThrustLander_R2/_stable_baselines3_version

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

ThrustLander_R2/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 0x7ff311830c10>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff311830ca0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff311830d30>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff311830dc0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7ff311830e50>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7ff311830ee0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff311830f70>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff311831000>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7ff311831090>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff311831120>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff3118311b0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff311831240>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7ff311826080>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 2523136,
+    "_total_timesteps": 2500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1690820859956202350,
+    "learning_rate": 0.001,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.009254400000000107,
+    "_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": 308,
+    "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": 32,
+    "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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"
+    }
+}

ThrustLander_R2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:25c476ab8e054a8a4474846302088d12687e66179fe826e4df97507437211bfa
+size 87929

ThrustLander_R2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1997580e26e798ce1520eaaeba55233b7e0c5c85b494b7e40bbd5b994efbfbd7
+size 43329

ThrustLander_R2/pytorch_variables.pth

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

ThrustLander_R2/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

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 0x7ff311830c10>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff311830ca0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff311830d30>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff311830dc0>", "_build": "<function ActorCriticPolicy._build at 0x7ff311830e50>", "forward": "<function ActorCriticPolicy.forward at 0x7ff311830ee0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff311830f70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff311831000>", "_predict": "<function ActorCriticPolicy._predict at 0x7ff311831090>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff311831120>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff3118311b0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff311831240>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ff311826080>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 2523136, "_total_timesteps": 2500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1690820859956202350, "learning_rate": 0.001, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.009254400000000107, "_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": 308, "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": 32, "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"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 272.7383189625947, "std_reward": 20.101124088955164, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-31T17:21:35.612389"}