Initial LunarLander model upload

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: 268.52 +/- 21.32
+      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 0x7f638588eb00>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f638588eb90>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f638588ec20>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f638588ecb0>", "_build": "<function ActorCriticPolicy._build at 0x7f638588ed40>", "forward": "<function ActorCriticPolicy.forward at 0x7f638588edd0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f638588ee60>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f638588eef0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f638588ef80>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f638588f010>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f638588f0a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f638588f130>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f6385886c80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1683642604322434497, "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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.10.11", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-LunarLander-v0.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0bda4b08354068b4d058af253cf1e864deb37b59e975937952517931d3980b96
+size 146743

ppo-LunarLander-v0/_stable_baselines3_version

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

ppo-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 0x7f638588eb00>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f638588eb90>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f638588ec20>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f638588ecb0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f638588ed40>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f638588edd0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f638588ee60>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f638588eef0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f638588ef80>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f638588f010>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f638588f0a0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f638588f130>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f6385886c80>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1683642604322434497,
+    "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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"
+    }
+}

ppo-LunarLander-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:22f0d4c0e2e412cbe4404ce39196a84d20fa6860383feaf363ecd7d706a9258f
+size 87929

ppo-LunarLander-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8489db357db53e7d79115fa390b033fe88f3b7a2d0dc3af84181f05864db76d5
+size 43329

ppo-LunarLander-v0/pytorch_variables.pth

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

ppo-LunarLander-v0/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.10.11
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.0+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": 268.5214060064958, "std_reward": 21.32424092624639, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-09T15:02:12.640781"}