Upload PPO LunarLander-v2 trained agent

README.md

@@ -1,3 +1,37 @@
 ---
-license: mit
+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: 278.49 +/- 18.47
+      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
+
+...
+```

SalmanHabeeb-moon-lander.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:63732a076a9a92c0880f474487556f9a116a6b9441d7cc02892896efb60f6e9b
+size 147112

SalmanHabeeb-moon-lander/_stable_baselines3_version

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

SalmanHabeeb-moon-lander/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 0x7f2b9c2c45e0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2b9c2c4670>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f2b9c2c4700>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f2b9c2c4790>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f2b9c2c4820>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f2b9c2c48b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f2b9c2c4940>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f2b9c2c49d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f2b9c2c4a60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f2b9c2c4af0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f2b9c2c4b80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f2b9c2baea0>"
+    },
+    "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": 3014656,
+    "_total_timesteps": 3000000.0,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1670485742367966291,
+    "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": -0.004885333333333408,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 736,
+    "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
+}

SalmanHabeeb-moon-lander/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dea0e5e66992a8bc91cfc9bd832410bc92cbd1a7f9d3377cd51d3f4174d520a9
+size 87929

SalmanHabeeb-moon-lander/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f90f229954fd1c9cd8b180281d6fdf0e1cfdc2d6d32d4971defdfc57f760c4a7
+size 43201

SalmanHabeeb-moon-lander/pytorch_variables.pth

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

SalmanHabeeb-moon-lander/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: 3.8.15
+Stable-Baselines3: 1.6.2
+PyTorch: 1.13.0+cu116
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

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 0x7f2b9c2c45e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f2b9c2c4670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f2b9c2c4700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f2b9c2c4790>", "_build": "<function ActorCriticPolicy._build at 0x7f2b9c2c4820>", "forward": "<function ActorCriticPolicy.forward at 0x7f2b9c2c48b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f2b9c2c4940>", "_predict": "<function ActorCriticPolicy._predict at 0x7f2b9c2c49d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f2b9c2c4a60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f2b9c2c4af0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f2b9c2c4b80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f2b9c2baea0>"}, "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": 3014656, "_total_timesteps": 3000000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1670485742367966291, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAADOzPz4U1q+8cxrguaJOUjj9EB2+yD8VOQAAgD8AAIA/urQUvitkVj+nsBW+pkIVv9xNFr6s65g8AAAAAAAAAACz93g9Kfx0upCV1LgKdRK0c7OBO2sU9zcAAAAAAAAAAHb/t74HAgY/ZsvMPUGqAb+Xq6q+gkhFPgAAAAAAAAAA8xb2ve0WFz5rO4o+iU0lvgmG4T19KKo9AAAAAAAAAAAawXy9qNa7P6dmQb836GI+fFfgPNbtHrwAAAAAAAAAAJqda70IIrI/Lu97vh6CmL477gO+bp2JvgAAAAAAAAAAmmVVPNKKybsMHsq9lfT8POMUGT0Cz9G9AACAPwAAgD+wPq0+Y/VZP+OFE76sLPq+OrOiPjZmtb4AAAAAAAAAAADfBD1Id4W68K7YOhq6jzaeSlo744FRNQAAAAAAAAAAmn08vGwX4Tyo7ls9DPGWvrOTdL2q/2E8AAAAAAAAAABmSyW9FASFuu1xAD3+GQG5WitDO5gP+LcAAIA/AACAP4ClDD2Hk0k/9rtPPfIJ/L6ZSzc9OpsmvAAAAAAAAAAAzU1EvQix4D18Eo899jCUvlDjiby2ZS08AAAAAAAAAAAAKgK8b12qP3QEwb36gAm/swyNOgdOlbwAAAAAAAAAAFoc9b07uIQ+ABu7Pto/X74caiM9kgKFPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.004885333333333408, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 736, "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-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.15", "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": 278.48659172353365, "std_reward": 18.47070766248041, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-08T08:28:34.060513"}