first model

README.md

@@ -0,0 +1,36 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 181.19 +/- 95.49
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+---
+
+# **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:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7f76a798ca70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f76a798cb00>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f76a798cb90>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f76a798cc20>", "_build": "<function ActorCriticPolicy._build at 0x7f76a798ccb0>", "forward": "<function ActorCriticPolicy.forward at 0x7f76a798cd40>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f76a798cdd0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f76a798ce60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f76a798cef0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f76a798cf80>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f76a7991050>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f76a79ea090>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 507904, "_total_timesteps": 500000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1657283270.1805453, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 124, "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, "system_info": {"OS": "Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1223c61d6a9332f2ab379f5058b0936e42704a5c3c06efe9e664f38d529d8952
+size 144142

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.5.0

ppo-LunarLander-v2/data

@@ -0,0 +1,94 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x7f76a798ca70>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f76a798cb00>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f76a798cb90>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f76a798cc20>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f76a798ccb0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f76a798cd40>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f76a798cdd0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f76a798ce60>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f76a798cef0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f76a798cf80>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f76a7991050>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f76a79ea090>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
+        ":serialized:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "num_timesteps": 507904,
+    "_total_timesteps": 500000.0,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1657283270.1805453,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gASVvwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAUsBSxNDBIgAUwCUToWUKYwBX5SFlIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCB9lH2UKGgXaA6MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgYjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.015808000000000044,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 124,
+    "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
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:450e30ae81ae09e4285582c9d43bfffe5d2f738937b925c2611fd31071ba461e
+size 84829

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e6fb0d9e9344f9008089387f0ba224126d4ea2e5b5c4abb94fa9f41b657b7e18
+size 43201

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 +1,7 @@
+OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
+Python: 3.7.13
+Stable-Baselines3: 1.5.0
+PyTorch: 1.11.0+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 181.1874336698726, "std_reward": 95.48950997501396, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-08T12:53:08.150997"}