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: -2.62 +/- 1.16
+      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:be512848fb284cadf6c78423fd954c687e27af831268331e96a7413f9d0e55a5
+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 0x7ff7fb3781f0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7ff7fb36fc00>"
+    },
+    "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": 1676487315450469841,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/RvAGjbi6x4WUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAIWfcPkXcj7wMqQ4/IWfcPkXcj7wMqQ4/IWfcPkXcj7wMqQ4/IWfcPkXcj7wMqQ4/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAA5ADTPfW4h7/P6J8/rtoyvwp5xT/lNi89EOSXv15Eor+to1s/2gCcv0ieuT+LG/O+lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAAAhZ9w+RdyPvAypDj+nloU7sfzAu/ziQjwhZ9w+RdyPvAypDj+nloU7sfzAu/ziQjwhZ9w+RdyPvAypDj+nloU7sfzAu/ziQjwhZ9w+RdyPvAypDj+nloU7sfzAu/ziQjyUaA5LBEsGhpRoEnSUUpR1Lg==",
+        "achieved_goal": "[[ 0.4304743  -0.01756109  0.55726695]\n [ 0.4304743  -0.01756109  0.55726695]\n [ 0.4304743  -0.01756109  0.55726695]\n [ 0.4304743  -0.01756109  0.55726695]]",
+        "desired_goal": "[[ 0.10302904 -1.060332    1.2492923 ]\n [-0.6986493   1.5427563   0.04277696]\n [-1.1866474  -1.2677114   0.85796624]\n [-1.218776    1.4501429  -0.4748195 ]]",
+        "observation": "[[ 0.4304743  -0.01756109  0.55726695  0.0040768  -0.0058895   0.01189494]\n [ 0.4304743  -0.01756109  0.55726695  0.0040768  -0.0058895   0.01189494]\n [ 0.4304743  -0.01756109  0.55726695  0.0040768  -0.0058895   0.01189494]\n [ 0.4304743  -0.01756109  0.55726695  0.0040768  -0.0058895   0.01189494]]"
+    },
+    "_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": "[[-0.08392121 -0.01453437  0.28863215]\n [ 0.07661892 -0.08039953  0.0604091 ]\n [ 0.01713521  0.14594129  0.08197634]\n [ 0.06168437  0.05765216  0.269239  ]]",
+        "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 sha256:30df2e8004760a85bdd9f5c84daf7eb3c4d7c928849536f85bb7c505eabaafa8
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:508e4b63e571d432afb898feae73c58bfb0de3ba9eb09d62f4e07ebe5a585240
+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 0x7ff7fb3781f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7ff7fb36fc00>"}, "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": 1676487315450469841, "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": "[[ 0.4304743  -0.01756109  0.55726695]\n [ 0.4304743  -0.01756109  0.55726695]\n [ 0.4304743  -0.01756109  0.55726695]\n [ 0.4304743  -0.01756109  0.55726695]]", "desired_goal": "[[ 0.10302904 -1.060332    1.2492923 ]\n [-0.6986493   1.5427563   0.04277696]\n [-1.1866474  -1.2677114   0.85796624]\n [-1.218776    1.4501429  -0.4748195 ]]", "observation": "[[ 0.4304743  -0.01756109  0.55726695  0.0040768  -0.0058895   0.01189494]\n [ 0.4304743  -0.01756109  0.55726695  0.0040768  -0.0058895   0.01189494]\n [ 0.4304743  -0.01756109  0.55726695  0.0040768  -0.0058895   0.01189494]\n [ 0.4304743  -0.01756109  0.55726695  0.0040768  -0.0058895   0.01189494]]"}, "_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": "[[-0.08392121 -0.01453437  0.28863215]\n [ 0.07661892 -0.08039953  0.0604091 ]\n [ 0.01713521  0.14594129  0.08197634]\n [ 0.06168437  0.05765216  0.269239  ]]", "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": -2.620829583425075, "std_reward": 1.1612136845580103, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-15T19:45:35.398216"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3c9453393c852ee99112674dc4d97e0bc6cadf679419bb75bc97b28758a05408
+size 3056