PPO Hyperparemeter tune 1M steps LL-2 agent

.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: 270.02 +/- 32.44
+      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
+
+...
+```

V4_PPO_LL.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ef5babdb3a9c6ce12a3f9ada46db969a7592318f68823883f1c8f71e7d72c6f2
+size 146480

V4_PPO_LL/_stable_baselines3_version

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

V4_PPO_LL/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 0x0000028CDDAA4DC0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x0000028CDDAA4E50>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x0000028CDDAA4EE0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x0000028CDDAA4F70>",
+        "_build": "<function ActorCriticPolicy._build at 0x0000028CDDAA3040>",
+        "forward": "<function ActorCriticPolicy.forward at 0x0000028CDDAA30D0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x0000028CDDAA3160>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x0000028CDDAA31F0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x0000028CDDAA3280>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x0000028CDDAA3310>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x0000028CDDAA33A0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x0000028CDDAA24B0>"
+    },
+    "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:": "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",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": "RandomState(MT19937)"
+    },
+    "n_envs": 1,
+    "num_timesteps": 1000448,
+    "_total_timesteps": 1000000.0,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1653918907.667849,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAJopKD44F40/bBvDPhLsIL98hGw+y1vXPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.00044800000000000395,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 3908,
+    "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": 32,
+    "n_epochs": 4,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

V4_PPO_LL/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dc8753666d04ed0020e82f24dd4a365fc30c33f11ee7065eb05e4fdadf911bf8
+size 84637

V4_PPO_LL/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d84443dd1e1c93988a732e9a94f5f0886d581efd2e8a00df3dc2f1b47e295c8a
+size 43073

V4_PPO_LL/pytorch_variables.pth

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

V4_PPO_LL/system_info.txt

@@ -0,0 +1,7 @@
+OS: Windows-10-10.0.19043-SP0 10.0.19043
+Python: 3.8.13
+Stable-Baselines3: 1.5.0
+PyTorch: 1.11.0+cpu
+GPU Enabled: False
+Numpy: 1.22.3
+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 0x0000028CDDAA4DC0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x0000028CDDAA4E50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x0000028CDDAA4EE0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x0000028CDDAA4F70>", "_build": "<function ActorCriticPolicy._build at 0x0000028CDDAA3040>", "forward": "<function ActorCriticPolicy.forward at 0x0000028CDDAA30D0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x0000028CDDAA3160>", "_predict": "<function ActorCriticPolicy._predict at 0x0000028CDDAA31F0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x0000028CDDAA3280>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x0000028CDDAA3310>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x0000028CDDAA33A0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x0000028CDDAA24B0>"}, "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:": "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", "n": 4, "_shape": [], "dtype": "int64", "_np_random": "RandomState(MT19937)"}, "n_envs": 1, "num_timesteps": 1000448, "_total_timesteps": 1000000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1653918907.667849, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAJopKD44F40/bBvDPhLsIL98hGw+y1vXPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00044800000000000395, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 3908, "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": 32, "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": "Windows-10-10.0.19043-SP0 10.0.19043", "Python": "3.8.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cpu", "GPU Enabled": "False", "Numpy": "1.22.3", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eac425697a62bbf52ea0298050fa371105dfd11c04f1b321ccdfd5a9a8abed9d
+size 196778

results.json

@@ -0,0 +1 @@
+{"mean_reward": 270.01934816885347, "std_reward": 32.44252887515386, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-30T16:23:02.219508"}