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: -3.75 +/- 1.36
+      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 sha256:5ee1421837e0c8ef654c77ac5cc8f5f4e237865b90b144d970e589d97e951110
+size 108147

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 0x7f7e80ab5ea0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f7e80abba40>"
+    },
+    "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": 1686228647566223568,
+    "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": "[[0.41715133 0.00327102 0.60852224]\n [0.41715133 0.00327102 0.60852224]\n [0.41715133 0.00327102 0.60852224]\n [0.41715133 0.00327102 0.60852224]]",
+        "desired_goal": "[[ 0.66453344 -0.5321792   1.1596888 ]\n [-0.01405159  0.33898097 -0.37355086]\n [-0.82653886  0.7153244   0.8218871 ]\n [ 0.4779978  -1.6594437   0.5756653 ]]",
+        "observation": "[[ 4.1715133e-01  3.2710165e-03  6.0852224e-01 -2.9286584e-03\n  -2.1477477e-05 -2.3095005e-03]\n [ 4.1715133e-01  3.2710165e-03  6.0852224e-01 -2.9286584e-03\n  -2.1477477e-05 -2.3095005e-03]\n [ 4.1715133e-01  3.2710165e-03  6.0852224e-01 -2.9286584e-03\n  -2.1477477e-05 -2.3095005e-03]\n [ 4.1715133e-01  3.2710165e-03  6.0852224e-01 -2.9286584e-03\n  -2.1477477e-05 -2.3095005e-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": "[[-0.03990596 -0.14255567  0.09182163]\n [ 0.1481193   0.0386356   0.23216812]\n [ 0.13454294 -0.02399982  0.02427154]\n [-0.07196994  0.07946221  0.2291562 ]]",
+        "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 sha256:62e9bdade610bd5acddd6d2a78cf0d3cb65f1f6bbbece41a4ed524bad8674d6c
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c4e71b49041cf6aea48b43a567414ca059f4ba76f82180609ea548f22fbcd3ec
+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: 3.10.11
+- 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 0x7f7e80ab5ea0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f7e80abba40>"}, "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": 1686228647566223568, "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": "[[0.41715133 0.00327102 0.60852224]\n [0.41715133 0.00327102 0.60852224]\n [0.41715133 0.00327102 0.60852224]\n [0.41715133 0.00327102 0.60852224]]", "desired_goal": "[[ 0.66453344 -0.5321792   1.1596888 ]\n [-0.01405159  0.33898097 -0.37355086]\n [-0.82653886  0.7153244   0.8218871 ]\n [ 0.4779978  -1.6594437   0.5756653 ]]", "observation": "[[ 4.1715133e-01  3.2710165e-03  6.0852224e-01 -2.9286584e-03\n  -2.1477477e-05 -2.3095005e-03]\n [ 4.1715133e-01  3.2710165e-03  6.0852224e-01 -2.9286584e-03\n  -2.1477477e-05 -2.3095005e-03]\n [ 4.1715133e-01  3.2710165e-03  6.0852224e-01 -2.9286584e-03\n  -2.1477477e-05 -2.3095005e-03]\n [ 4.1715133e-01  3.2710165e-03  6.0852224e-01 -2.9286584e-03\n  -2.1477477e-05 -2.3095005e-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": "[[-0.03990596 -0.14255567  0.09182163]\n [ 0.1481193   0.0386356   0.23216812]\n [ 0.13454294 -0.02399982  0.02427154]\n [-0.07196994  0.07946221  0.2291562 ]]", "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, "system_info": {"OS": "Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.11", "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": -3.7539671721868215, "std_reward": 1.3593786151888674, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-06-08T14:01:59.478863"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9d8067888467285472e0de75643a4b1eb8897280797ccb6a5f86d83767ed2958
+size 2387