Upload my PPO LunarLander-v2 trained agent

ForAllMankind2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e28f5374fe51768f50a0d79d4307a79c04e7123516b03bef8c9b30474364fb49
+size 149040

ForAllMankind2/_stable_baselines3_version

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

ForAllMankind2/data

@@ -0,0 +1,100 @@
+{
+    "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 0x7fcbdf68b670>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcbdf68b700>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcbdf68b790>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcbdf68b820>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fcbdf68b8b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fcbdf68b940>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fcbdf68b9d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcbdf68ba60>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fcbdf68baf0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcbdf68bb80>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcbdf68bc10>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcbdf68bca0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fcbdf681ba0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVbwAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SMEHRvcmNoLm9wdGltLmFkYW2UjARBZGFtlJOUjA1hY3RpdmF0aW9uX2ZulIwbdG9yY2gubm4ubW9kdWxlcy5hY3RpdmF0aW9ulIwJTGVha3lSZUxVlJOUdS4=",
+        "optimizer_class": "<class 'torch.optim.adam.Adam'>",
+        "activation_fn": "<class 'torch.nn.modules.activation.LeakyReLU'>"
+    },
+    "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": 3014656,
+    "_total_timesteps": 3000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1677154080633368512,
+    "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": 1000000,
+    "_current_progress_remaining": -0.004885333333333408,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 920,
+    "n_steps": 2048,
+    "gamma": 0.995,
+    "gae_lambda": 0.97,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

ForAllMankind2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:08a448fb05e708c6ead51024c3570ebf9fea9facc736e6b99463588f058d6794
+size 88057

ForAllMankind2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:429da97e42cc4189cec76532480c24bdfd432d8d05665242c4cc0c28c5e35767
+size 43393

ForAllMankind2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

ForAllMankind2/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: 1.22.4
+- Gym: 0.21.0

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: 289.66 +/- 14.43
+      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 0x7fcbdf68b670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcbdf68b700>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcbdf68b790>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcbdf68b820>", "_build": "<function ActorCriticPolicy._build at 0x7fcbdf68b8b0>", "forward": "<function ActorCriticPolicy.forward at 0x7fcbdf68b940>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fcbdf68b9d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcbdf68ba60>", "_predict": "<function ActorCriticPolicy._predict at 0x7fcbdf68baf0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcbdf68bb80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcbdf68bc10>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcbdf68bca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fcbdf681ba0>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVbwAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SMEHRvcmNoLm9wdGltLmFkYW2UjARBZGFtlJOUjA1hY3RpdmF0aW9uX2ZulIwbdG9yY2gubm4ubW9kdWxlcy5hY3RpdmF0aW9ulIwJTGVha3lSZUxVlJOUdS4=", "optimizer_class": "<class 'torch.optim.adam.Adam'>", "activation_fn": "<class 'torch.nn.modules.activation.LeakyReLU'>"}, "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": 3014656, "_total_timesteps": 3000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1677154080633368512, "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": 1000000, "_current_progress_remaining": -0.004885333333333408, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 920, "n_steps": 2048, "gamma": 0.995, "gae_lambda": 0.97, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "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": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 289.663773417237, "std_reward": 14.427822393266505, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-23T13:27:41.005730"}