LunarLander-v2 first try

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: 250.80 +/- 23.44
+      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 0x7a01f17be200>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a01f17be290>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a01f17be320>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7a01f17be3b0>", "_build": "<function ActorCriticPolicy._build at 0x7a01f17be440>", "forward": "<function ActorCriticPolicy.forward at 0x7a01f17be4d0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7a01f17be560>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7a01f17be5f0>", "_predict": "<function ActorCriticPolicy._predict at 0x7a01f17be680>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7a01f17be710>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7a01f17be7a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7a01f17be830>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7a01f195f700>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1698834932531359816, "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-nlamot.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c873cf4c5619d1a47b994ab469f9c625a9ca708c24e02989e6ae95e2f5c5809c
+size 148050

ppo-lunarlander-nlamot/_stable_baselines3_version

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

ppo-lunarlander-nlamot/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 0x7a01f17be200>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7a01f17be290>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7a01f17be320>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7a01f17be3b0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7a01f17be440>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7a01f17be4d0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7a01f17be560>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7a01f17be5f0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7a01f17be680>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7a01f17be710>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7a01f17be7a0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7a01f17be830>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7a01f195f700>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1698834932531359816,
+    "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

ppo-lunarlander-nlamot/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2fc75917cccf8d36298f03cb2098fa4c742f73f06cbadaf5142b07a7203e027f
+size 88362

ppo-lunarlander-nlamot/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:59dbe753b4c6a32ba97be9e11b6602f8326317c0bc8c6f424caabe09ba44ddd6
+size 43762

ppo-lunarlander-nlamot/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

ppo-lunarlander-nlamot/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": 250.8002133, "std_reward": 23.43679641406432, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-01T11:14:02.344126"}