Upload PPO SB3 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: 215.90 +/- 19.41
+      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 0x7d93ba2bc1f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d93ba2bc280>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d93ba2bc310>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d93ba2bc3a0>", "_build": "<function ActorCriticPolicy._build at 0x7d93ba2bc430>", "forward": "<function ActorCriticPolicy.forward at 0x7d93ba2bc4c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d93ba2bc550>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d93ba2bc5e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7d93ba2bc670>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d93ba2bc700>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d93ba2bc790>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d93ba2bc820>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d93ba25aac0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1715334559086611842, "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-6.1.58+-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": "True", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-SB3-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ae4069e9603350a6e91882c5c36c0066d0b51537a171ac33ef22d5db6a1fbfe5
+size 148088

ppo-SB3-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

ppo-SB3-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 0x7d93ba2bc1f0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d93ba2bc280>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d93ba2bc310>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d93ba2bc3a0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7d93ba2bc430>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7d93ba2bc4c0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d93ba2bc550>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d93ba2bc5e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7d93ba2bc670>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d93ba2bc700>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d93ba2bc790>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d93ba2bc820>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7d93ba25aac0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1715334559086611842,
+    "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-SB3-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7f97977d375072e4c85534d3c7ead5e14c056b77e93f2d015f435af35765eab5
+size 88362

ppo-SB3-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b2b1b4bd25406c80f7edf836362bd178f625e99469da69ce29848eb7fb06fb5
+size 43762

ppo-SB3-LunarLander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

ppo-SB3-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: 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": 215.90333070000003, "std_reward": 19.413936595984637, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-05-10T10:13:16.330355"}