Upload 13 files

.gitattributes

@@ -25,7 +25,6 @@
 *.safetensors filter=lfs diff=lfs merge=lfs -text
 saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
 *.wasm filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v2
+      type: PandaReachDense-v2
+    metrics:
+    - type: mean_reward
+      value: -0.48 +/- 0.14
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaReachDense-v2**
+This is a trained model of a **A2C** agent playing **PandaReachDense-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
+
+...
+```

a2c-PandaReachDense-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e54aab7d525df641dd1f85a00e753a5fad9f4a8afec54260bd1e41ec8060febd
+size 131

a2c-PandaReachDense-v2/_stable_baselines3_version

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

a2c-PandaReachDense-v2/data

@@ -0,0 +1,96 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    MultiInputActorClass 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 (Tuple)\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: Uses the CombinedExtractor\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 MultiInputActorCriticPolicy.__init__ at 0x7ff23e7c9040>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7ff23e7c1ae0>"
+    },
+    "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.dict.Dict'>",
+        ":serialized:": "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",
+        "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])",
+        "_shape": null,
+        "dtype": null,
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            3
+        ],
+        "low": "[-1. -1. -1.]",
+        "high": "[1. 1. 1.]",
+        "bounded_below": "[ True  True  True]",
+        "bounded_above": "[ True  True  True]",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1673967563972419255,
+    "learning_rate": 0.001,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[0.2513818  0.03876952 0.5852905 ]\n [0.2513818  0.03876952 0.5852905 ]\n [0.2513818  0.03876952 0.5852905 ]\n [0.2513818  0.03876952 0.5852905 ]]",
+        "desired_goal": "[[ 1.2135438  -0.6738663   1.6695023 ]\n [-0.9141476   0.6051988  -0.2645693 ]\n [-1.0723549   0.6880129   1.0846335 ]\n [-0.09830251 -1.4138633  -1.3425976 ]]",
+        "observation": "[[0.2513818  0.03876952 0.5852905  0.07261704 0.00586898 0.07258803]\n [0.2513818  0.03876952 0.5852905  0.07261704 0.00586898 0.07258803]\n [0.2513818  0.03876952 0.5852905  0.07261704 0.00586898 0.07258803]\n [0.2513818  0.03876952 0.5852905  0.07261704 0.00586898 0.07258803]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]",
+        "desired_goal": "[[ 0.00438312  0.12263557  0.19670582]\n [ 0.14086299 -0.08954176  0.01360141]\n [-0.02564871 -0.08105079  0.15575132]\n [ 0.00543616 -0.05532974  0.13640961]]",
+        "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"
+    },
+    "_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": 31250,
+    "n_steps": 8,
+    "gamma": 0.99,
+    "gae_lambda": 0.9,
+    "ent_coef": 0.001,
+    "vf_coef": 0.4,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": false
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c3b099d4de9e69bc606b2c5a6a7b3bc511bce567c445e029ae1ab1e8302f5777
+size 130

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e4197e0a155c463570075bc3e75f77ca08a1630c2a1aa4a164601a0754259377
+size 130

a2c-PandaReachDense-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:99153ed9b545f0f5af916841c8510b36c9a0a84e88f412678bced8aba994b482
+size 128

a2c-PandaReachDense-v2/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.0+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- Gym: 0.21.0

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    MultiInputActorClass 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 (Tuple)\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: Uses the CombinedExtractor\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 MultiInputActorCriticPolicy.__init__ at 0x7ff23e7c9040>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7ff23e7c1ae0>"}, "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.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1673967563972419255, "learning_rate": 0.001, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[0.2513818  0.03876952 0.5852905 ]\n [0.2513818  0.03876952 0.5852905 ]\n [0.2513818  0.03876952 0.5852905 ]\n [0.2513818  0.03876952 0.5852905 ]]", "desired_goal": "[[ 1.2135438  -0.6738663   1.6695023 ]\n [-0.9141476   0.6051988  -0.2645693 ]\n [-1.0723549   0.6880129   1.0846335 ]\n [-0.09830251 -1.4138633  -1.3425976 ]]", "observation": "[[0.2513818  0.03876952 0.5852905  0.07261704 0.00586898 0.07258803]\n [0.2513818  0.03876952 0.5852905  0.07261704 0.00586898 0.07258803]\n [0.2513818  0.03876952 0.5852905  0.07261704 0.00586898 0.07258803]\n [0.2513818  0.03876952 0.5852905  0.07261704 0.00586898 0.07258803]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]", "desired_goal": "[[ 0.00438312  0.12263557  0.19670582]\n [ 0.14086299 -0.08954176  0.01360141]\n [-0.02564871 -0.08105079  0.15575132]\n [ 0.00543616 -0.05532974  0.13640961]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"}, "_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": 31250, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.001, "vf_coef": 0.4, "max_grad_norm": 0.5, "normalize_advantage": false, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.4774048477935139, "std_reward": 0.14327652029487803, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-17T15:53:22.752766"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b5288984089ddfd428bf3508d88b1daacf605d8f0aa1c3e22185b9e2e2e95128
+size 129