Upload model

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: 269.51 +/- 17.12
+      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 0x7b707d88cb80>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b707d88cc10>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b707d88cca0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b707d88cd30>", "_build": "<function ActorCriticPolicy._build at 0x7b707d88cdc0>", "forward": "<function ActorCriticPolicy.forward at 0x7b707d88ce50>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b707d88cee0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b707d88cf70>", "_predict": "<function ActorCriticPolicy._predict at 0x7b707d88d000>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b707d88d090>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b707d88d120>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b707d88d1b0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7b707e73a780>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1717853057112690900, "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "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:addffea5a4404dc1151149ae42f50fd66c9a44adace043f7ab88d1f3b375880b
+size 148012

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 0x7b707d88cb80>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b707d88cc10>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b707d88cca0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b707d88cd30>",
+        "_build": "<function ActorCriticPolicy._build at 0x7b707d88cdc0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7b707d88ce50>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b707d88cee0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b707d88cf70>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7b707d88d000>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b707d88d090>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b707d88d120>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b707d88d1b0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7b707e73a780>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1717853057112690900,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAAAxpTxId5e6SCpzN7eVgjIxWTY60JiMtgAAgD8AAIA/c37GPfA0jD/vGY89pH75vnywAD75EIq8AAAAAAAAAAAAEfA8nUyuP8Z2iT76L62+m2Y3PSukLT4AAAAAAAAAAM1Ucry4Dsi5sA12t8E/6rI7hCY7jv6RNgAAgD8AAIA/E+AuvqV5Fj8OVok+HpXLvnAEnT2nVws9AAAAAAAAAABza6U9ol1NPjrGyb1vV7W+xEiEPV2m1TwAAAAAAAAAAE0FLr5Il5c/qaCPvpG69r5pCXG+cuijvQAAAAAAAAAA2qjUPZO8JD/eB648mzWtvoTWej1dEBG9AAAAAAAAAAAz2Uc9H72guY7HBrtNZEG1ty06O7p4ujQAAIA/AACAP6aG571cEtY+5o+nPq/Isr61Y9g9oQZLPQAAAAAAAAAAzWplPAqXYDgtqNg7vkEds2qvJ7zKZwazAACAPwAAgD+aXQM8c3q1PwwghD6RHZo9q3FIuwYjrDwAAAAAAAAAABq3JT0f8du7+NE2vO04mjzQIyk9yIiBvQAAgD8AAIA/wHeCPjHJPj+2Ady9rlnTvlkiFT7Qe2G+AAAAAAAAAABNwTe9YswnPsfhDj5L/2u+w+M1PWLSGz0AAAAAAAAAAKB3cj6jYjQ/4y/mPBZgzr7lQFk++n+pvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="
+    },
+    "_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-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:906d859b7e7ad4d6b611fad2ce07d0eb041e60ed54b98e35df998b11cf02e7e2
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5db658b3eaafa3edb1ea7e380a9121f9e9d171f336bc6d588f41240e4ceee8da
+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": 269.5071623, "std_reward": 17.11783751605397, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-06-08T13:48:05.880240"}