Initial commit, slm friends

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- AntBulletEnv-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: AntBulletEnv-v0
+      type: AntBulletEnv-v0
+    metrics:
+    - type: mean_reward
+      value: 1447.38 +/- 304.16
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **AntBulletEnv-v0**
+This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
+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
+
+...
+```

a2c-AntBulletEnv-v0.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:49dd36df0068ba849c7bdaf380a8663d0db2b128a76ab610f1b655a99d090825
+size 129260

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,106 @@
+{
+    "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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7f5f4d86c160>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5f4d86c1f0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5f4d86c280>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5f4d86c310>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f5f4d86c3a0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f5f4d86c430>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f5f4d86c4c0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5f4d86c550>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f5f4d86c5e0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5f4d86c670>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5f4d86c700>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5f4d86c790>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f5f4d862ab0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu",
+        "log_std_init": -2,
+        "ortho_init": false,
+        "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
+        "optimizer_kwargs": {
+            "alpha": 0.99,
+            "eps": 1e-05,
+            "weight_decay": 0
+        }
+    },
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            28
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]",
+        "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAIC/AACAvwAAgL8AAIC/AACAvwAAgL8AAIC/AACAv5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIA/AACAPwAAgD8AAIA/AACAPwAAgD8AAIA/AACAP5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAQEBAQEBAQGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAEBAQEBAQEBlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]",
+        "high": "[1. 1. 1. 1. 1. 1. 1. 1.]",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "num_timesteps": 3000000,
+    "_total_timesteps": 3000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1674218536420511905,
+    "learning_rate": 0.00096,
+    "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:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_episode_num": 0,
+    "use_sde": true,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.0,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 93750,
+    "n_steps": 8,
+    "gamma": 0.99,
+    "gae_lambda": 0.9,
+    "ent_coef": 0.0,
+    "vf_coef": 0.4,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": false
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8bb65289d4fe3c9944a0228866d67b17495996198fa0c1a991f38056fa6de466
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:255227ad5860640fa26354ca6f96a5e9bd38831c9e055f54398674e36df00d47
+size 56958

a2c-AntBulletEnv-v0/pytorch_variables.pth

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

a2c-AntBulletEnv-v0/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7f5f4d86c160>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5f4d86c1f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5f4d86c280>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5f4d86c310>", "_build": "<function ActorCriticPolicy._build at 0x7f5f4d86c3a0>", "forward": "<function ActorCriticPolicy.forward at 0x7f5f4d86c430>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f5f4d86c4c0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5f4d86c550>", "_predict": "<function ActorCriticPolicy._predict at 0x7f5f4d86c5e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5f4d86c670>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5f4d86c700>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5f4d86c790>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f5f4d862ab0>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [28], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1. 1. 1.]", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 3000000, "_total_timesteps": 3000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1674218536420511905, "learning_rate": 0.00096, "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:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_episode_num": 0, "use_sde": true, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 93750, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "max_grad_norm": 0.5, "normalize_advantage": false, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "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 sha256:6b35013ca5afa5cd61b90b8270c430a7b049d30726355e5e6b1337d10ea0225e
+size 1232043

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1447.3761132762302, "std_reward": 304.161701074167, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-20T14:07:55.194129"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bd799b1041fe1f420da186d5ae7cda26dc4417138ea2db8b99862dd8c662218f
+size 2136