Upload PPO LunarLander-v2 trained 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: 268.33 +/- 13.51
+      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 0x7bfb22e20b80>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7bfb22e20c10>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7bfb22e20ca0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7bfb22e20d30>", "_build": "<function ActorCriticPolicy._build at 0x7bfb22e20dc0>", "forward": "<function ActorCriticPolicy.forward at 0x7bfb22e20e50>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7bfb22e20ee0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7bfb22e20f70>", "_predict": "<function ActorCriticPolicy._predict at 0x7bfb22e21000>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7bfb22e21090>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7bfb22e21120>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7bfb22e211b0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7bfb22e1c940>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1714997921837524590, "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:": "gAWVdgIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoCIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoESiWCAAAAAAAAAABAQEBAQEBAZRoFUsIhZRoGXSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBEoliAAAAAAAAAAAAC0wgAAtMIAAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaAtLCIWUaBl0lFKUjARoaWdolGgRKJYgAAAAAAAAAAAAtEIAALRCAACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgLSwiFlGgZdJRSlIwIbG93X3JlcHKUjFtbLTkwLiAgICAgICAgLTkwLiAgICAgICAgIC01LiAgICAgICAgIC01LiAgICAgICAgIC0zLjE0MTU5MjcgIC01LgogIC0wLiAgICAgICAgIC0wLiAgICAgICBdlIwJaGlnaF9yZXBylIxTWzkwLiAgICAgICAgOTAuICAgICAgICAgNS4gICAgICAgICA1LiAgICAgICAgIDMuMTQxNTkyNyAgNS4KICAxLiAgICAgICAgIDEuICAgICAgIF2UjApfbnBfcmFuZG9tlE51Yi4=", "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": "False", "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 sha256:96714e84972fdec088e9a9b6eaceeafc407429534608e41ee7bd63689c14de25
+size 147509

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 0x7bfb22e20b80>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7bfb22e20c10>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7bfb22e20ca0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7bfb22e20d30>",
+        "_build": "<function ActorCriticPolicy._build at 0x7bfb22e20dc0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7bfb22e20e50>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7bfb22e20ee0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7bfb22e20f70>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7bfb22e21000>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7bfb22e21090>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7bfb22e21120>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7bfb22e211b0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7bfb22e1c940>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1714997921837524590,
+    "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 sha256:3715db70d4f6fce48f9d95cd751733293481c2d53b6c6acb144bbd6f93d0c75b
+size 87978

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cfb8c3477c9f9c99ef1372827113ee84f6b00f599ddae16b3c71c2a042145770
+size 43634

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: False
+- Numpy: 1.25.2
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 268.32989824289257, "std_reward": 13.5092938292185, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-05-06T12:50:51.092651"}