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: -4.58 +/- 1.72
+      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 sha256:8917e8906c9674bdf21d13b3b0f712644c9aba635b22e80bbecd236f1aea0380
+size 108023

a2c-PandaReachDense-v2/_stable_baselines3_version

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

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 0x7fd25255ab80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fd2525569c0>"
+    },
+    "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": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1677668437292349727,
+    "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": "[[ 0.42255723 -0.01794806  0.48085344]\n [ 0.42255723 -0.01794806  0.48085344]\n [ 0.42255723 -0.01794806  0.48085344]\n [ 0.42255723 -0.01794806  0.48085344]]",
+        "desired_goal": "[[-1.1616303   1.7242337  -0.68927485]\n [-0.22268371 -0.96014327  0.90718085]\n [ 0.7328746  -1.2853911  -0.69273317]\n [ 1.2573553  -0.2279618  -1.1194694 ]]",
+        "observation": "[[ 0.42255723 -0.01794806  0.48085344  0.00687583 -0.00702271  0.00952766]\n [ 0.42255723 -0.01794806  0.48085344  0.00687583 -0.00702271  0.00952766]\n [ 0.42255723 -0.01794806  0.48085344  0.00687583 -0.00702271  0.00952766]\n [ 0.42255723 -0.01794806  0.48085344  0.00687583 -0.00702271  0.00952766]]"
+    },
+    "_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": "[[ 0.02315432 -0.06967434  0.16827117]\n [-0.05745096 -0.06441408  0.13475044]\n [ 0.06472189 -0.11507181  0.15461566]\n [-0.11460432 -0.13147399  0.15549572]]",
+        "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": 50000,
+    "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 sha256:55b0fd3c8b02b49fbefaf59d11ae7357a47f1daa78a024250c5375b4356d328c
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b11f8f747146d678e97437d1a78f2620387bfde604c47b2cb59c13a32ec8fe11
+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-5.10.147+-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: 1.22.4
+- 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 0x7fd25255ab80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fd2525569c0>"}, "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": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1677668437292349727, "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": "[[ 0.42255723 -0.01794806  0.48085344]\n [ 0.42255723 -0.01794806  0.48085344]\n [ 0.42255723 -0.01794806  0.48085344]\n [ 0.42255723 -0.01794806  0.48085344]]", "desired_goal": "[[-1.1616303   1.7242337  -0.68927485]\n [-0.22268371 -0.96014327  0.90718085]\n [ 0.7328746  -1.2853911  -0.69273317]\n [ 1.2573553  -0.2279618  -1.1194694 ]]", "observation": "[[ 0.42255723 -0.01794806  0.48085344  0.00687583 -0.00702271  0.00952766]\n [ 0.42255723 -0.01794806  0.48085344  0.00687583 -0.00702271  0.00952766]\n [ 0.42255723 -0.01794806  0.48085344  0.00687583 -0.00702271  0.00952766]\n [ 0.42255723 -0.01794806  0.48085344  0.00687583 -0.00702271  0.00952766]]"}, "_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": "[[ 0.02315432 -0.06967434  0.16827117]\n [-0.05745096 -0.06441408  0.13475044]\n [ 0.06472189 -0.11507181  0.15461566]\n [-0.11460432 -0.13147399  0.15549572]]", "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": 50000, "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-5.10.147+-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": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -4.583369048405439, "std_reward": 1.7188832802425382, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-01T11:57:49.759427"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:95919bb602eb365f3bf6db6033263cd2f654f041876642ca815fc8072a1c269c
+size 3056