This LunarLander-v2-PPO-0 is my first submission to the HF Hub!

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,28 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: LunarLander-v2-PPO-0
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 78.31 +/- 95.33
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+---
+
+  # **LunarLander-v2-PPO-0** Agent playing **LunarLander-v2**
+  This is a trained model of a **LunarLander-v2-PPO-0** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+  
+  ## Usage (with Stable-baselines3)
+  TODO: Add your code
+  

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 0x7f24736a3f80>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f247362b050>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f247362b0e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f247362b170>", "_build": "<function ActorCriticPolicy._build at 0x7f247362b200>", "forward": "<function ActorCriticPolicy.forward at 0x7f247362b290>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f247362b320>", "_predict": "<function ActorCriticPolicy._predict at 0x7f247362b3b0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f247362b440>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f247362b4d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f247362b560>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f247366bcc0>"}, "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": 2031616, "_total_timesteps": 2000000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652143566.6691415, "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": 1240, "n_steps": 2048, "gamma": 0.97, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 20, "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"}}

mikes_first_lander-1.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:85ea5e085a3f68af16ac471a621d7b438c6bdbecd3c931b7b1d527895ad4e858
+size 144112

mikes_first_lander-1/_stable_baselines3_version

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

mikes_first_lander-1/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 0x7f24736a3f80>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f247362b050>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f247362b0e0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f247362b170>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f247362b200>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f247362b290>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f247362b320>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f247362b3b0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f247362b440>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f247362b4d0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f247362b560>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f247366bcc0>"
+    },
+    "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": 2031616,
+    "_total_timesteps": 2000000.0,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1652143566.6691415,
+    "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": 1240,
+    "n_steps": 2048,
+    "gamma": 0.97,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 20,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

mikes_first_lander-1/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5542a739d72dd3abc67291302ae7f07bbb60734b8494345e6af64be6f8d77d90
+size 84893

mikes_first_lander-1/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f8723cb77654ff52a1a1f79f94860bfbe8f362d344653d8492e2fcd4633bc418
+size 43201

mikes_first_lander-1/pytorch_variables.pth

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

mikes_first_lander-1/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

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:37d2a4ee52760d12211f02d90f0f9b072fb28e1f27f366aebbe7c4850d55e0b4
+size 206427

results.json

@@ -0,0 +1 @@
+{"mean_reward": 78.30728392871998, "std_reward": 95.3295919232362, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-10T02:36:28.115572"}