Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v3
+      type: PandaReachDense-v3
+    metrics:
+    - type: mean_reward
+      value: -0.21 +/- 0.12
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaReachDense-v3**
+This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
+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-v3.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9ed5e45c70f6e35b06507a0504eb0943029820142f82fd01fd25837e161c7c85
+size 106831

a2c-PandaReachDense-v3/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0

a2c-PandaReachDense-v3/data

@@ -0,0 +1,97 @@
+{
+    "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 0x7a8b7ef41b40>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7a8b7ef442c0>"
+    },
+    "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": 1691938203106088226,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 0.2652215  -0.00441321  0.43658376]\n [ 0.2652215  -0.00441321  0.43658376]\n [ 0.2652215  -0.00441321  0.43658376]\n [ 0.2652215  -0.00441321  0.43658376]]",
+        "desired_goal": "[[-0.88251376  0.5332668  -0.39915487]\n [ 0.36571002  1.2710768   0.6739465 ]\n [-1.4237447  -0.41936922  0.6321502 ]\n [-1.2070895   0.08373944  1.3692702 ]]",
+        "observation": "[[ 0.2652215  -0.00441321  0.43658376  0.48093605  0.00224208  0.3814398 ]\n [ 0.2652215  -0.00441321  0.43658376  0.48093605  0.00224208  0.3814398 ]\n [ 0.2652215  -0.00441321  0.43658376  0.48093605  0.00224208  0.3814398 ]\n [ 0.2652215  -0.00441321  0.43658376  0.48093605  0.00224208  0.3814398 ]]"
+    },
+    "_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": "[[ 0.10519563 -0.09787682  0.21783096]\n [ 0.06344548 -0.07091144  0.11870947]\n [-0.13836342  0.13473085  0.01363633]\n [-0.0573574   0.06619446  0.18982105]]",
+        "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 'gymnasium.spaces.dict.Dict'>",
+        ":serialized:": "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",
+        "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])",
+        "_shape": null,
+        "dtype": null,
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True]",
+        "bounded_above": "[ True  True  True]",
+        "_shape": [
+            3
+        ],
+        "low": "[-1. -1. -1.]",
+        "high": "[1. 1. 1.]",
+        "low_repr": "-1.0",
+        "high_repr": "1.0",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

a2c-PandaReachDense-v3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3d7d64cd6ccc79da4df2f84fb1568de5c6df8f74ed96762506083a5040a29bff
+size 44734

a2c-PandaReachDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9f74774a3a88e7845a330c6547a83a413eba6f5dee4582a4d99afc6f94ec91b0
+size 46014

a2c-PandaReachDense-v3/pytorch_variables.pth

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

a2c-PandaReachDense-v3/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

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 0x7a8b7ef41b40>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7a8b7ef442c0>"}, "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": 1691938203106088226, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.2652215  -0.00441321  0.43658376]\n [ 0.2652215  -0.00441321  0.43658376]\n [ 0.2652215  -0.00441321  0.43658376]\n [ 0.2652215  -0.00441321  0.43658376]]", "desired_goal": "[[-0.88251376  0.5332668  -0.39915487]\n [ 0.36571002  1.2710768   0.6739465 ]\n [-1.4237447  -0.41936922  0.6321502 ]\n [-1.2070895   0.08373944  1.3692702 ]]", "observation": "[[ 0.2652215  -0.00441321  0.43658376  0.48093605  0.00224208  0.3814398 ]\n [ 0.2652215  -0.00441321  0.43658376  0.48093605  0.00224208  0.3814398 ]\n [ 0.2652215  -0.00441321  0.43658376  0.48093605  0.00224208  0.3814398 ]\n [ 0.2652215  -0.00441321  0.43658376  0.48093605  0.00224208  0.3814398 ]]"}, "_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": "[[ 0.10519563 -0.09787682  0.21783096]\n [ 0.06344548 -0.07091144  0.11870947]\n [-0.13836342  0.13473085  0.01363633]\n [-0.0573574   0.06619446  0.18982105]]", "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 'gymnasium.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "gAWVnQEAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAwAAAAAAAAABAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUjAFDlHSUUpSMDWJvdW5kZWRfYWJvdmWUaBEolgMAAAAAAAAAAQEBlGgVSwOFlGgZdJRSlIwGX3NoYXBllEsDhZSMA2xvd5RoESiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaAtLA4WUaBl0lFKUjARoaWdolGgRKJYMAAAAAAAAAAAAgD8AAIA/AACAP5RoC0sDhZRoGXSUUpSMCGxvd19yZXBylIwELTEuMJSMCWhpZ2hfcmVwcpSMAzEuMJSMCl9ucF9yYW5kb22UTnViLg==", "dtype": "float32", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "low_repr": "-1.0", "high_repr": "1.0", "_np_random": null}, "n_envs": 4, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.21319190533831717, "std_reward": 0.12372763922528804, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-13T15:52:04.856110"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:939841eebcb67e98308cb2aff80b53815e6b87802b5c3d78cd09fa3d1ce5895c
+size 2623