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: -3.78 +/- 0.95
+      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 sha256:0d993440b653ff9b3a6950569964547346694b8709f97a1553f1f8d1c32414b4
+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 0x7f26a6876160>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f26a6873210>"
+    },
+    "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": 1674382915807785778,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAec+tPvcoVbuOLwo/ec+tPvcoVbuOLwo/ec+tPvcoVbuOLwo/ec+tPvcoVbuOLwo/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAA4ajZvx7woD4dhGO/ULJbP+5akb/Kr4u/l/gkv3xOaL6I5mS/IK2FPmN/qb/IbNM/lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAAB5z60+9yhVu44vCj8n4ki84JM4u5YMUDp5z60+9yhVu44vCj8n4ki84JM4u5YMUDp5z60+9yhVu44vCj8n4ki84JM4u5YMUDp5z60+9yhVu44vCj8n4ki84JM4u5YMUDqUaA5LBEsGhpRoEnSUUpR1Lg==",
+        "achieved_goal": "[[ 0.33947352 -0.00325256  0.5397881 ]\n [ 0.33947352 -0.00325256  0.5397881 ]\n [ 0.33947352 -0.00325256  0.5397881 ]\n [ 0.33947352 -0.00325256  0.5397881 ]]",
+        "desired_goal": "[[-1.7004663   0.31433195 -0.88873464]\n [ 0.8581896  -1.1355875  -1.0913022 ]\n [-0.6444182  -0.2268619  -0.8941426 ]\n [ 0.26108646 -1.3242      1.6517572 ]]",
+        "observation": "[[ 0.33947352 -0.00325256  0.5397881  -0.01226095 -0.00281643  0.00079364]\n [ 0.33947352 -0.00325256  0.5397881  -0.01226095 -0.00281643  0.00079364]\n [ 0.33947352 -0.00325256  0.5397881  -0.01226095 -0.00281643  0.00079364]\n [ 0.33947352 -0.00325256  0.5397881  -0.01226095 -0.00281643  0.00079364]]"
+    },
+    "_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": "[[-0.10740348  0.02753086  0.13912337]\n [-0.14034149 -0.14280462  0.27719828]\n [ 0.02253063 -0.0734255   0.10612582]\n [-0.03075367 -0.07217755  0.0579209 ]]",
+        "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 sha256:333f9983d7d1d495d7e0da719e9efbb015960aefadec53da6e4df35ec3b303a5
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8f1233e2221dec5a57fcb9b0d61c9e9b8b70d39866d45ee40cc24f1ccfbe908f
+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: 1.21.6
+- 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 0x7f26a6876160>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f26a6873210>"}, "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": 1674382915807785778, "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": "[[ 0.33947352 -0.00325256  0.5397881 ]\n [ 0.33947352 -0.00325256  0.5397881 ]\n [ 0.33947352 -0.00325256  0.5397881 ]\n [ 0.33947352 -0.00325256  0.5397881 ]]", "desired_goal": "[[-1.7004663   0.31433195 -0.88873464]\n [ 0.8581896  -1.1355875  -1.0913022 ]\n [-0.6444182  -0.2268619  -0.8941426 ]\n [ 0.26108646 -1.3242      1.6517572 ]]", "observation": "[[ 0.33947352 -0.00325256  0.5397881  -0.01226095 -0.00281643  0.00079364]\n [ 0.33947352 -0.00325256  0.5397881  -0.01226095 -0.00281643  0.00079364]\n [ 0.33947352 -0.00325256  0.5397881  -0.01226095 -0.00281643  0.00079364]\n [ 0.33947352 -0.00325256  0.5397881  -0.01226095 -0.00281643  0.00079364]]"}, "_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": "[[-0.10740348  0.02753086  0.13912337]\n [-0.14034149 -0.14280462  0.27719828]\n [ 0.02253063 -0.0734255   0.10612582]\n [-0.03075367 -0.07217755  0.0579209 ]]", "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": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -3.7788405127823355, "std_reward": 0.9461024881193254, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-22T11:06:10.466211"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:86e0f0e2c2e765ad8903ab438adc40405fddcf55d78400296ee72ef75b4ea4ae
+size 3056