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: 263.27 +/- 25.11
+      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 0x7fa312e8b820>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa312e8b8b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa312e8b940>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa312e8b9d0>", "_build": "<function ActorCriticPolicy._build at 0x7fa312e8ba60>", "forward": "<function ActorCriticPolicy.forward at 0x7fa312e8baf0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa312e8bb80>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa312e8bc10>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa312e8bca0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa312e8bd30>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa312e8bdc0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa312e8be50>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fa312e859f0>"}, "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:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1674264823352940705, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_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, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 300, "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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "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 sha256:83d225c6addcd052dcbec7657bbc55597be6d8c7f43e88d3b1c8cad00edbaa1b
+size 147412

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

ppo-LunarLander-v2/data

@@ -0,0 +1,95 @@
+{
+    "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 0x7fa312e8b820>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa312e8b8b0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa312e8b940>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa312e8b9d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fa312e8ba60>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fa312e8baf0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa312e8bb80>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa312e8bc10>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fa312e8bca0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa312e8bd30>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa312e8bdc0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa312e8be50>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fa312e859f0>"
+    },
+    "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:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1674264823352940705,
+    "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
+    },
+    "_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:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 300,
+    "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 sha256:1faeb4838ec90ecfc198e7fe7455fea42de6753bff4a93a02bfca33699eff11d
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b95f3b2a757213979c6f100f914ad9b56dfda172bb166a6d1a4627aa53ad030e
+size 43393

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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 263.27138916539207, "std_reward": 25.108849203309184, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-21T02:08:16.105309"}