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: -2.66 +/- 0.38
+      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 sha256:8552101345010327aa30f79efb696f80c1982d4539baec8dded13f89428237f3
+size 108159

a2c-PandaReachDense-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

a2c-PandaReachDense-v2/data

@@ -0,0 +1,95 @@
+{
+    "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 0x7f82bd78dd80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f82bd782e00>"
+    },
+    "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
+        }
+    },
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1689124010133854403,
+    "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": "[[ 0.3507739  -0.02371371  0.55234885]\n [ 0.3507739  -0.02371371  0.55234885]\n [ 0.3507739  -0.02371371  0.55234885]\n [ 0.3507739  -0.02371371  0.55234885]]",
+        "desired_goal": "[[ 1.3961239  -0.87527627 -0.4691788 ]\n [-1.4060503   0.54891694 -0.23223051]\n [-1.6089684   0.19361328  1.1174343 ]\n [-0.8391651   0.2144735   1.157556  ]]",
+        "observation": "[[ 3.5077390e-01 -2.3713710e-02  5.5234885e-01  6.1608684e-05\n  -2.2047877e-03 -6.2265410e-03]\n [ 3.5077390e-01 -2.3713710e-02  5.5234885e-01  6.1608684e-05\n  -2.2047877e-03 -6.2265410e-03]\n [ 3.5077390e-01 -2.3713710e-02  5.5234885e-01  6.1608684e-05\n  -2.2047877e-03 -6.2265410e-03]\n [ 3.5077390e-01 -2.3713710e-02  5.5234885e-01  6.1608684e-05\n  -2.2047877e-03 -6.2265410e-03]]"
+    },
+    "_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": "[[-0.00715645  0.00687092  0.18870193]\n [-0.11815196  0.13704145  0.06177195]\n [-0.03594797 -0.0611396   0.01496365]\n [-0.04828247 -0.07862727  0.2568837 ]]",
+        "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,
+    "_stats_window_size": 100,
+    "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,
+    "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
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eef84d37282766db22dca7c1001ea832e9eb63c60e701479084db4f342daaf09
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:43b449de4e2705309157befea792c7c2a89256dcd1f9b13d8754cca339011083
+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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.1+cu118
+- 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 0x7f82bd78dd80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f82bd782e00>"}, "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}}, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1689124010133854403, "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": "[[ 0.3507739  -0.02371371  0.55234885]\n [ 0.3507739  -0.02371371  0.55234885]\n [ 0.3507739  -0.02371371  0.55234885]\n [ 0.3507739  -0.02371371  0.55234885]]", "desired_goal": "[[ 1.3961239  -0.87527627 -0.4691788 ]\n [-1.4060503   0.54891694 -0.23223051]\n [-1.6089684   0.19361328  1.1174343 ]\n [-0.8391651   0.2144735   1.157556  ]]", "observation": "[[ 3.5077390e-01 -2.3713710e-02  5.5234885e-01  6.1608684e-05\n  -2.2047877e-03 -6.2265410e-03]\n [ 3.5077390e-01 -2.3713710e-02  5.5234885e-01  6.1608684e-05\n  -2.2047877e-03 -6.2265410e-03]\n [ 3.5077390e-01 -2.3713710e-02  5.5234885e-01  6.1608684e-05\n  -2.2047877e-03 -6.2265410e-03]\n [ 3.5077390e-01 -2.3713710e-02  5.5234885e-01  6.1608684e-05\n  -2.2047877e-03 -6.2265410e-03]]"}, "_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": "[[-0.00715645  0.00687092  0.18870193]\n [-0.11815196  0.13704145  0.06177195]\n [-0.03594797 -0.0611396   0.01496365]\n [-0.04828247 -0.07862727  0.2568837 ]]", "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, "_stats_window_size": 100, "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, "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:": "gAWVcwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLA4WUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaAtLA4WUjAFDlHSUUpSMBGhpZ2iUaBMolgwAAAAAAAAAAACAPwAAgD8AAIA/lGgLSwOFlGgWdJRSlIwNYm91bmRlZF9iZWxvd5RoEyiWAwAAAAAAAAABAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUaBZ0lFKUjA1ib3VuZGVkX2Fib3ZllGgTKJYDAAAAAAAAAAEBAZRoIksDhZRoFnSUUpSMCl9ucF9yYW5kb22UTnViLg==", "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, "system_info": {"OS": "Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -2.659247813280672, "std_reward": 0.3802397678295552, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-12T01:55:20.620309"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7d1ed133782a71f1194aeb68c9a0b5216b3379ac28fc12fc0ef51a1d65d69b7d
+size 2387