Unit 1 lunar lander trained

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: 263.19 +/- 12.78
+      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 0x796226828d30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x796226828dc0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x796226828e50>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x796226828ee0>", "_build": "<function ActorCriticPolicy._build at 0x796226828f70>", "forward": "<function ActorCriticPolicy.forward at 0x796226829000>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x796226829090>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x796226829120>", "_predict": "<function ActorCriticPolicy._predict at 0x7962268291b0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x796226829240>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7962268292d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x796226829360>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x796226972e00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1712804477388771270, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAAb3Hr72P1S8XiXhvAFZb7vhVr09v0NDPAAAgD8AAIA/AF8wvc05Sj55g4O9MxB9vpYwW70vO0g9AAAAAAAAAAAza/+7V9B7PoA4H77EwJ2+SQ+VvQje3rwAAAAAAAAAABpjFb3PU2S8Mo9APPSGIj0chBM8itxFPQAAgD8AAIA/gEUsPYVT1rmjzn6zDmE0sKWhPLvuAbozAACAPwAAgD9mGuc7Zb4SP1L1or3kec++xx1NvejpCL0AAAAAAAAAAM0+gTyvpTc/p2eJvfOHwr7fiVI9ylD7OwAAAAAAAAAAACNrPYnbpz8yfKk+o4e0vrt37T1JB6E+AAAAAAAAAAAAM189v0GoP07A4z4BIu2+7i4NPfJBTz4AAAAAAAAAAA1TeT5Nrw0/964LvqAKz741gNQ9QDmVvQAAAAAAAAAA8868vSlsKbqg1pI3PueGMmOH/DqSdai2AACAPwAAgD+av7A9ALueP5W0Bz7Ms+K+qCw+PuY/CzsAAAAAAAAAALOAKT00T8w+Xm4vvsb8yr56SO+9yvuAuwAAAAAAAAAAzUfPPADivz+ed9o9hvf8vU8CMzwKKqo8AAAAAAAAAAAzOee9BNWzP5UJF7/72Gy++Xzqvf78zr4AAAAAAAAAAAADmLwhPMa8V3uXvNl4PT1fOye+TZeSvAAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.015808000000000044, "_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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "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-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.2.1+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a5fd30d6da6ea57d48dcfa827acf67080ba702ba9940dcd6149f2bb75ef03b38
+size 148068

ppo-LunarLander-v2/_stable_baselines3_version

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

ppo-LunarLander-v2/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 0x796226828d30>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x796226828dc0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x796226828e50>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x796226828ee0>",
+        "_build": "<function ActorCriticPolicy._build at 0x796226828f70>",
+        "forward": "<function ActorCriticPolicy.forward at 0x796226829000>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x796226829090>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x796226829120>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7962268291b0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x796226829240>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7962268292d0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x796226829360>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x796226972e00>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1712804477388771270,
+    "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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "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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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0a994433b9478bd60e554c788614bcda7de9a476ee749a6fab5f9dd40209c81f
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e5ce87c3376fd4321b2a7e48947b0839cf9270afd67048e12d37041dc30246e7
+size 43762

ppo-LunarLander-v2/pytorch_variables.pth

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

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.2.1+cu121
+- GPU Enabled: True
+- Numpy: 1.25.2
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 263.18615639999996, "std_reward": 12.780580808312468, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-04-11T03:36:41.992389"}