test commit of ppo-LL-v2

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: 247.38 +/- 16.78
+      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 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 0x7f9da7e563a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9da7e56430>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9da7e564c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9da7e56550>", "_build": "<function ActorCriticPolicy._build at 0x7f9da7e565e0>", "forward": "<function ActorCriticPolicy.forward at 0x7f9da7e56670>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9da7e56700>", "_predict": "<function ActorCriticPolicy._predict at 0x7f9da7e56790>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9da7e56820>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9da7e568b0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9da7e56940>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f9da7e517e0>"}, "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": 64, "num_timesteps": 2097152, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1672055464603208633, "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:": "gAWVswAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJZAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiS0CFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.04857599999999995, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 256, "n_steps": 2048, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 128, "n_epochs": 16, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+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 sha256:4ad2390ae8bb75ed3c5bf4e2b59a763326be4847ccb7cfcf9f3eefc847bf6ca2
+size 149311

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.2

ppo-LunarLander-v2/data

@@ -0,0 +1,94 @@
+{
+    "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 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 0x7f9da7e563a0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9da7e56430>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9da7e564c0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9da7e56550>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f9da7e565e0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f9da7e56670>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9da7e56700>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f9da7e56790>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9da7e56820>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9da7e568b0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9da7e56940>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f9da7e517e0>"
+    },
+    "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": 64,
+    "num_timesteps": 2097152,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1672055464603208633,
+    "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:": "gAWVswAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJZAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiS0CFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.04857599999999995,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 256,
+    "n_steps": 2048,
+    "gamma": 0.999,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 128,
+    "n_epochs": 16,
+    "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 sha256:431352d9288e5a08965cbdc17064dfce064af90c2ed35eb776c8d35ecf99e280
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7c40461acc5d35fcdfa744f2915c3c851fae9dd1b8dd97ad0bd5fd804732f538
+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-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.8.16
+Stable-Baselines3: 1.6.2
+PyTorch: 1.13.0+cu116
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 247.37803213059033, "std_reward": 16.775300864084244, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-26T12:57:28.864429"}