Initial upload of LunarLander PPO agent

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: 278.63 +/- 14.86
+      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 0x7ea23a61feb0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ea23a61ff40>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ea23a634040>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ea23a6340d0>", "_build": "<function ActorCriticPolicy._build at 0x7ea23a634160>", "forward": "<function ActorCriticPolicy.forward at 0x7ea23a6341f0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ea23a634280>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ea23a634310>", "_predict": "<function ActorCriticPolicy._predict at 0x7ea23a6343a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ea23a634430>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ea23a6344c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ea23a634550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ea1df500ec0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1690199899806817554, "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-5.15.109+-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"}}

ppo-lunarlander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:50c8bc3d99d89f70f5e2fe38cd8d00e897fb8a681be67d41224f3967d8a8ad53
+size 146713

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 0x7ea23a61feb0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ea23a61ff40>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ea23a634040>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ea23a6340d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7ea23a634160>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7ea23a6341f0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ea23a634280>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ea23a634310>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7ea23a6343a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ea23a634430>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ea23a6344c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ea23a634550>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7ea1df500ec0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1690199899806817554,
+    "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-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:29657b6c8b50472eb008063b9da036603caf96802e4e3444f89028682e4cbb0e
+size 87929

ppo-lunarlander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:28c1f563c84d4073226063ca1f8249d8ff5c2a26a2688acffec9ab988580b846
+size 43329

ppo-lunarlander-v2/pytorch_variables.pth

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

ppo-lunarlander-v2/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

results.json

@@ -0,0 +1 @@
+{"mean_reward": 278.63130979999994, "std_reward": 14.85510438147324, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-24T12:29:27.448631"}