First upload to the 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: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 220.30 +/- 22.97
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+---
+
+  # **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
+  

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 0x7fb7ef6158c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb7ef615950>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb7ef6159e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb7ef615a70>", "_build": "<function ActorCriticPolicy._build at 0x7fb7ef615b00>", "forward": "<function ActorCriticPolicy.forward at 0x7fb7ef615b90>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb7ef615c20>", "_predict": "<function ActorCriticPolicy._predict at 0x7fb7ef615cb0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb7ef615d40>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb7ef615dd0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb7ef615e60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fb7ef5f0060>"}, "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": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652235015.829387, "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": 124, "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-5.4.188+-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"}}

ppo_first_model.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0e4f47e527b75b9a91c4cb29f6367db18f9ab12cc4414eb07d3922618173f224
+size 144019

ppo_first_model/_stable_baselines3_version

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

ppo_first_model/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 0x7fb7ef6158c0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb7ef615950>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb7ef6159e0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb7ef615a70>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fb7ef615b00>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fb7ef615b90>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb7ef615c20>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fb7ef615cb0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb7ef615d40>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb7ef615dd0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb7ef615e60>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fb7ef5f0060>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu",
+        "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": 507904,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1652235015.829387,
+    "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": 124,
+    "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_first_model/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:60c1e43dcb7a23570f10437b6c3516ea8afa857ab30a942964f6c618eb91bcad
+size 84829

ppo_first_model/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:94cfc8c8de3b7379be2b0967c094c179446409869432abddf35c3b9be5cc00f6
+size 43201

ppo_first_model/pytorch_variables.pth

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

ppo_first_model/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 sha256:723d2947bd477807b99395e68b7daf22b02ab848bc03d50d4ff5191440ef94b3
+size 174816

results.json

@@ -0,0 +1 @@
+{"mean_reward": 220.30203946342095, "std_reward": 22.973322982344037, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-11T02:23:56.539714"}