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: 257.69 +/- 20.71
+      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 0x7f89369adbd0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f89369adc60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f89369adcf0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f89369add80>", "_build": "<function ActorCriticPolicy._build at 0x7f89369ade10>", "forward": "<function ActorCriticPolicy.forward at 0x7f89369adea0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f89369adf30>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f89369adfc0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f89369ae050>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f89369ae0e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f89369ae170>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f89369ae200>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f893694d440>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1700439367478516119, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAE1k2L0jGUw/U73hPGmbgr4gMC+9nZ5uvAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00044800000000000395, "_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": 3908, "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": 1, "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-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "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:ac8d4d7240370da87f130623e9be0541fe4a85c81c1645f9cc76d84b743b7765
+size 147396

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 0x7f89369adbd0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f89369adc60>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f89369adcf0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f89369add80>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f89369ade10>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f89369adea0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f89369adf30>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f89369adfc0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f89369ae050>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f89369ae0e0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f89369ae170>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f89369ae200>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f893694d440>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1000448,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1700439367478516119,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAE1k2L0jGUw/U73hPGmbgr4gMC+9nZ5uvAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.00044800000000000395,
+    "_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": 3908,
+    "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": 1,
+    "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 sha256:79ad879d7bc1e8af6cc09463e40d65f08c31822d6729fa6ffb02c4cb28246ce9
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:310d89bcb625e057f2bbccd4ea46fce48b835364966a35811dba33c894c3706c
+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-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.1.0+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 257.6940850939009, "std_reward": 20.70943067712745, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-20T00:54:07.133497"}