Initial commit

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: -4.13 +/- 0.92
+      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:e0f503f361ea9540b5d4a3a1f54f947dd3c968cc1376c32b2a5d85fd0d82c0f4
+size 108097

a2c-PandaReachDense-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0a11

a2c-PandaReachDense-v2/data

@@ -0,0 +1,94 @@
+{
+    "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 0x7f9812574550>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f9812568cc0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=",
+        "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": 2000000,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1676099947444745934,
+    "learning_rate": 0.0007,
+    "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": "[[0.49039426 0.00094179 0.5597961 ]\n [0.49039426 0.00094179 0.5597961 ]\n [0.49039426 0.00094179 0.5597961 ]\n [0.49039426 0.00094179 0.5597961 ]]",
+        "desired_goal": "[[-0.9140857  -1.324542   -0.15502451]\n [-0.61017275 -1.0347977  -0.13146006]\n [ 1.330587    1.5806395  -0.92426974]\n [-0.93663615 -1.3428766  -0.31927684]]",
+        "observation": "[[ 0.49039426  0.00094179  0.5597961   0.0095656  -0.00346394  0.00753629]\n [ 0.49039426  0.00094179  0.5597961   0.0095656  -0.00346394  0.00753629]\n [ 0.49039426  0.00094179  0.5597961   0.0095656  -0.00346394  0.00753629]\n [ 0.49039426  0.00094179  0.5597961   0.0095656  -0.00346394  0.00753629]]"
+    },
+    "_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": "[[-0.12814832  0.01434149  0.00710845]\n [-0.02438467  0.02728174  0.15325765]\n [ 0.01005032  0.02703392  0.22908983]\n [ 0.03395649 -0.03132558  0.0325275 ]]",
+        "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": false,
+    "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": 100000,
+    "n_steps": 5,
+    "gamma": 0.99,
+    "gae_lambda": 1.0,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "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 sha256:09409cddb991a942419bc72a2caf131818d705f69eefe2e004dbfa13c1825544
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d58f2d4f141bc7b02e64c2ac12951a8f74a5ef72d8f3703000261b390b78bc3e
+size 46014

a2c-PandaReachDense-v2/pytorch_variables.pth

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

a2c-PandaReachDense-v2/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.15.0-58-generic-x86_64-with-glibc2.17 # 64~20.04.1-Ubuntu SMP Fri Jan 6 16:42:31 UTC 2023
+- Python: 3.8.15
+- Stable-Baselines3: 1.7.0a11
+- PyTorch: 1.13.1+cu117
+- GPU Enabled: True
+- Numpy: 1.23.5
+- 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 0x7f9812574550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f9812568cc0>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "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:": "gAWVbQEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLA4WUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaApLA4WUjAFDlHSUUpSMBGhpZ2iUaBIolgwAAAAAAAAAAACAPwAAgD8AAIA/lGgKSwOFlGgVdJRSlIwNYm91bmRlZF9iZWxvd5RoEiiWAwAAAAAAAAABAQGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYDAAAAAAAAAAEBAZRoIUsDhZRoFXSUUpSMCl9ucF9yYW5kb22UTnViLg==", "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": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1676099947444745934, "learning_rate": 0.0007, "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": "[[0.49039426 0.00094179 0.5597961 ]\n [0.49039426 0.00094179 0.5597961 ]\n [0.49039426 0.00094179 0.5597961 ]\n [0.49039426 0.00094179 0.5597961 ]]", "desired_goal": "[[-0.9140857  -1.324542   -0.15502451]\n [-0.61017275 -1.0347977  -0.13146006]\n [ 1.330587    1.5806395  -0.92426974]\n [-0.93663615 -1.3428766  -0.31927684]]", "observation": "[[ 0.49039426  0.00094179  0.5597961   0.0095656  -0.00346394  0.00753629]\n [ 0.49039426  0.00094179  0.5597961   0.0095656  -0.00346394  0.00753629]\n [ 0.49039426  0.00094179  0.5597961   0.0095656  -0.00346394  0.00753629]\n [ 0.49039426  0.00094179  0.5597961   0.0095656  -0.00346394  0.00753629]]"}, "_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": "[[-0.12814832  0.01434149  0.00710845]\n [-0.02438467  0.02728174  0.15325765]\n [ 0.01005032  0.02703392  0.22908983]\n [ 0.03395649 -0.03132558  0.0325275 ]]", "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": false, "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": 100000, "n_steps": 5, "gamma": 0.99, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "system_info": {"OS": "Linux-5.15.0-58-generic-x86_64-with-glibc2.17 # 64~20.04.1-Ubuntu SMP Fri Jan 6 16:42:31 UTC 2023", "Python": "3.8.15", "Stable-Baselines3": "1.7.0a11", "PyTorch": "1.13.1+cu117", "GPU Enabled": "True", "Numpy": "1.23.5", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -4.133633702713996, "std_reward": 0.9184906004922099, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-11T16:15:43.553718"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e88e18f0aba523289bc371659beaefccb715c1b2b7834b7292549cb366ebfd29
+size 3056