First commit

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: -3.48 +/- 21.17
+      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 0x7840fd7985e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7840fd798670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7840fd798700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7840fd798790>", "_build": "<function ActorCriticPolicy._build at 0x7840fd798820>", "forward": "<function ActorCriticPolicy.forward at 0x7840fd7988b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7840fd798940>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7840fd7989d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7840fd798a60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7840fd798af0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7840fd798b80>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7840fd798c10>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7840fd733ac0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 16384, "_total_timesteps": 1000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1715773775083247696, "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": -15.384, "_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": 11, "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-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ba17a5b807468d6297e89b435e733b1472cd37ac2989619180fdb8909f637b23
+size 147932

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 0x7840fd7985e0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7840fd798670>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7840fd798700>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7840fd798790>",
+        "_build": "<function ActorCriticPolicy._build at 0x7840fd798820>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7840fd7988b0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7840fd798940>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7840fd7989d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7840fd798a60>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7840fd798af0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7840fd798b80>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7840fd798c10>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7840fd733ac0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 16384,
+    "_total_timesteps": 1000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1715773775083247696,
+    "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": -15.384,
+    "_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": 11,
+    "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:f2ccb38e18e24000597e23807f23548c893a193cc2a9a6609eda07867feef160
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3aed7f26a223c57a2e6bcbf2f7c0d2bbc86dd7232246981c682527740cce7e38
+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-6.1.58+-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": -3.4847964999999994, "std_reward": 21.168767626577683, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-05-15T12:03:23.762807"}