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: 241.97 +/- 23.45
+      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 0x7d8e60865ea0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d8e60865f30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d8e60865fc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d8e60866050>", "_build": "<function ActorCriticPolicy._build at 0x7d8e608660e0>", "forward": "<function ActorCriticPolicy.forward at 0x7d8e60866170>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d8e60866200>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d8e60866290>", "_predict": "<function ActorCriticPolicy._predict at 0x7d8e60866320>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d8e608663b0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d8e60866440>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d8e608664d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d8e6080ec00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1718166576724745646, "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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"}, "system_info": {"OS": "Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Apr 28 14:29:16 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.3.0+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 sha256:4314eb65d16cc54c85e5725a588f398da6e007de755ccf8150e259039b5bfad5
+size 148080

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 0x7d8e60865ea0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d8e60865f30>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d8e60865fc0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d8e60866050>",
+        "_build": "<function ActorCriticPolicy._build at 0x7d8e608660e0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7d8e60866170>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d8e60866200>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d8e60866290>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7d8e60866320>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d8e608663b0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d8e60866440>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d8e608664d0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7d8e6080ec00>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1718166576724745646,
+    "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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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:84d7e09c23bf0e076a3597db029e57c0215ae4cb8515d626d964c04ce0ca6444
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d900b87f514e8e50d48f193f8a40782431ab5cd519408827aad1a5cebd561fd9
+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-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Apr 28 14:29:16 UTC 2024
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.3.0+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": 241.96675277141048, "std_reward": 23.446474876609436, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-06-12T05:01:06.545951"}