another bad model hehe

.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 +1,36 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 78.32 +/- 104.77
+      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
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7f900ce4ed40>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f900ce4edd0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f900ce4ee60>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f900ce4eef0>", "_build": "<function ActorCriticPolicy._build at 0x7f900ce4ef80>", "forward": "<function ActorCriticPolicy.forward at 0x7f900ce53050>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f900ce530e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f900ce53170>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f900ce53200>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f900ce53290>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f900ce53320>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f900ce21600>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gASVwwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBVudW1weS5jb3JlLm11bHRpYXJyYXmUjAxfcmVjb25zdHJ1Y3SUk5RoBowHbmRhcnJheZSTlEsAhZRDAWKUh5RSlChLAUsIhZRoColDIAAAgP8AAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/lHSUYowEaGlnaJRoEmgUSwCFlGgWh5RSlChLAUsIhZRoColDIAAAgH8AAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/lHSUYowNYm91bmRlZF9iZWxvd5RoEmgUSwCFlGgWh5RSlChLAUsIhZRoB4wCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDCAAAAAAAAAAAlHSUYowNYm91bmRlZF9hYm92ZZRoEmgUSwCFlGgWh5RSlChLAUsIhZRoKolDCAAAAAAAAAAAlHSUYowKX25wX3JhbmRvbZROdWIu", "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:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 262144, "_total_timesteps": 250000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1653321097.9959404, "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:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="}, "_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:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 64, "n_steps": 2048, "gamma": 0.2, "gae_lambda": 0.2, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 128, "n_epochs": 8, "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"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9d047626a579f630bf4c82f35b0c9bacaf332fed03e8576d54fdf135514e8645
+size 269608

results.json

@@ -0,0 +1 @@
+{"mean_reward": 78.32007933102525, "std_reward": 104.7663662090126, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-23T15:57:19.563140"}

second_rl_ppo_model.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c6ebddc8a392a2d56fe4befbcab044ce00d08a5b3b0b2dc1987b5622a9014591
+size 144044

second_rl_ppo_model/_stable_baselines3_version

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

second_rl_ppo_model/data

@@ -0,0 +1,94 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__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 0x7f900ce4ed40>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f900ce4edd0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f900ce4ee60>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f900ce4eef0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f900ce4ef80>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f900ce53050>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f900ce530e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f900ce53170>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f900ce53200>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f900ce53290>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f900ce53320>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f900ce21600>"
+    },
+    "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:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "num_timesteps": 262144,
+    "_total_timesteps": 250000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1653321097.9959404,
+    "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:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="
+    },
+    "_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:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 64,
+    "n_steps": 2048,
+    "gamma": 0.2,
+    "gae_lambda": 0.2,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 128,
+    "n_epochs": 8,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

second_rl_ppo_model/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f257661425a56a84cb259a7dc685c663752b3bf8702a3990c8d53ac5c82ddd49
+size 84829

second_rl_ppo_model/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1d99a8dec70897c582de235d6daf4c3231be81f6b126571eee9483834330cc95
+size 43201

second_rl_ppo_model/pytorch_variables.pth

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

second_rl_ppo_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