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: -2.45 +/- 0.59
+      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 sha256:9d5679d82f09390e6fa1f935b6ce322dbf99890557f6e72d8b4699230d3eab09
+size 108112

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 0x7fe7529f7e50>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fe7529fd500>"
+    },
+    "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": 1679534007280765026,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA0iTYPuQMlbt5FQk/0iTYPuQMlbt5FQk/0iTYPuQMlbt5FQk/0iTYPuQMlbt5FQk/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAU47BP1660j/EV5A/Zd2cv6efGj9cFbQ/8dNPP5RtxT/cviq/u5Kgv6UPsL62Ap8+lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAADSJNg+5AyVu3kVCT/Z5Jo8obY2uWjxcDzSJNg+5AyVu3kVCT/Z5Jo8obY2uWjxcDzSJNg+5AyVu3kVCT/Z5Jo8obY2uWjxcDzSJNg+5AyVu3kVCT/Z5Jo8obY2uWjxcDyUaA5LBEsGhpRoEnSUUpR1Lg==",
+        "achieved_goal": "[[ 0.42215592 -0.00454866  0.5354839 ]\n [ 0.42215592 -0.00454866  0.5354839 ]\n [ 0.42215592 -0.00454866  0.5354839 ]\n [ 0.42215592 -0.00454866  0.5354839 ]]",
+        "desired_goal": "[[ 1.5121559   1.6463125   1.1276784 ]\n [-1.2255064   0.6039986   1.4069018 ]\n [ 0.8118277   1.5424066  -0.6669748 ]\n [-1.2544779  -0.34386936  0.31056756]]",
+        "observation": "[[ 4.2215592e-01 -4.5486558e-03  5.3548390e-01  1.8907951e-02\n  -1.7424907e-04  1.4705993e-02]\n [ 4.2215592e-01 -4.5486558e-03  5.3548390e-01  1.8907951e-02\n  -1.7424907e-04  1.4705993e-02]\n [ 4.2215592e-01 -4.5486558e-03  5.3548390e-01  1.8907951e-02\n  -1.7424907e-04  1.4705993e-02]\n [ 4.2215592e-01 -4.5486558e-03  5.3548390e-01  1.8907951e-02\n  -1.7424907e-04  1.4705993e-02]]"
+    },
+    "_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": "[[-0.084111   -0.1414138   0.00355141]\n [ 0.01020465  0.09186385  0.14641732]\n [ 0.06027867  0.12442396  0.09445381]\n [-0.10425124  0.04627118  0.09186143]]",
+        "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 sha256:5e762b74b8993ee923ffd55f1f13b3e14772704292cdc1e72cf3a18bcd1471b6
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:da9a378c624e8ab5f54fcfbf4fdf4ea68161d42e74acd0bdd99c830e1c418907
+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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- 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 0x7fe7529f7e50>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fe7529fd500>"}, "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": 1679534007280765026, "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": "[[ 0.42215592 -0.00454866  0.5354839 ]\n [ 0.42215592 -0.00454866  0.5354839 ]\n [ 0.42215592 -0.00454866  0.5354839 ]\n [ 0.42215592 -0.00454866  0.5354839 ]]", "desired_goal": "[[ 1.5121559   1.6463125   1.1276784 ]\n [-1.2255064   0.6039986   1.4069018 ]\n [ 0.8118277   1.5424066  -0.6669748 ]\n [-1.2544779  -0.34386936  0.31056756]]", "observation": "[[ 4.2215592e-01 -4.5486558e-03  5.3548390e-01  1.8907951e-02\n  -1.7424907e-04  1.4705993e-02]\n [ 4.2215592e-01 -4.5486558e-03  5.3548390e-01  1.8907951e-02\n  -1.7424907e-04  1.4705993e-02]\n [ 4.2215592e-01 -4.5486558e-03  5.3548390e-01  1.8907951e-02\n  -1.7424907e-04  1.4705993e-02]\n [ 4.2215592e-01 -4.5486558e-03  5.3548390e-01  1.8907951e-02\n  -1.7424907e-04  1.4705993e-02]]"}, "_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": "[[-0.084111   -0.1414138   0.00355141]\n [ 0.01020465  0.09186385  0.14641732]\n [ 0.06027867  0.12442396  0.09445381]\n [-0.10425124  0.04627118  0.09186143]]", "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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "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": -2.4532884852960706, "std_reward": 0.5883278615834258, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-23T02:05:45.809133"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:31d7073554d618deb01c3513f849434175aa009d2466b16c7813d02abb58f7ec
+size 3056