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: 254.12 +/- 16.40
+      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 0x7b5956982b00>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b5956982b90>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b5956982c20>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b5956982cb0>", "_build": "<function ActorCriticPolicy._build at 0x7b5956982d40>", "forward": "<function ActorCriticPolicy.forward at 0x7b5956982dd0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b5956982e60>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b5956982ef0>", "_predict": "<function ActorCriticPolicy._predict at 0x7b5956982f80>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b5956983010>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b59569830a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b5956983130>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7b595698c580>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1690410056459434550, "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 sha256:937877424b79f321bad927c588044195bb47fa0acd33aa736012870fb20bc87c
+size 146745

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 0x7b5956982b00>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b5956982b90>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b5956982c20>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b5956982cb0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7b5956982d40>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7b5956982dd0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b5956982e60>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b5956982ef0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7b5956982f80>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b5956983010>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b59569830a0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b5956983130>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7b595698c580>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1690410056459434550,
+    "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 sha256:41de60e26e511a88f596fa538cd059f6e72f66497113c1d9b82ec66345dac3de
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:39176642d047f4855a27cc0199577c344a5e3c6dddb6a5b804560268a5ab5860
+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": 254.11861620000005, "std_reward": 16.40482084193537, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-26T22:45:53.969586"}