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: -2.63 +/- 0.55
+      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:c9dc91fee4b10150120ecf970c6d60dd012135c7d782d71932a45eea7299ec47
+size 108022

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 0x7f91c3881a60>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f91c3884180>"
+    },
+    "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": 1681243041046645936,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/RvAGjbi6x4WUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[0.4220477  0.01685923 0.5363604 ]\n [0.4220477  0.01685923 0.5363604 ]\n [0.4220477  0.01685923 0.5363604 ]\n [0.4220477  0.01685923 0.5363604 ]]",
+        "desired_goal": "[[-0.96025753  1.5334127   1.4478735 ]\n [-1.2304481   0.30287498  1.715569  ]\n [ 1.6933564  -1.0674157   0.8876728 ]\n [-1.744577   -0.29134285  0.7646427 ]]",
+        "observation": "[[0.4220477  0.01685923 0.5363604  0.01358718 0.0013675  0.0102217 ]\n [0.4220477  0.01685923 0.5363604  0.01358718 0.0013675  0.0102217 ]\n [0.4220477  0.01685923 0.5363604  0.01358718 0.0013675  0.0102217 ]\n [0.4220477  0.01685923 0.5363604  0.01358718 0.0013675  0.0102217 ]]"
+    },
+    "_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": "[[-0.13569383  0.03330506  0.18253784]\n [ 0.14352335  0.02696251  0.28770018]\n [ 0.09753665 -0.13780774  0.29847294]\n [ 0.13972832  0.04255902  0.0990178 ]]",
+        "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 sha256:9c903b2a3ae291415edf5f957a39ed596984690f8d65ff6703f8f1279b5b34b7
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:67063962717e37e165b89d247d18b1a058ad956ce80008ce4290173a702bbb94
+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: 1.8.0
+- PyTorch: 2.0.0+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 0x7f91c3881a60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f91c3884180>"}, "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": 1681243041046645936, "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": "[[0.4220477  0.01685923 0.5363604 ]\n [0.4220477  0.01685923 0.5363604 ]\n [0.4220477  0.01685923 0.5363604 ]\n [0.4220477  0.01685923 0.5363604 ]]", "desired_goal": "[[-0.96025753  1.5334127   1.4478735 ]\n [-1.2304481   0.30287498  1.715569  ]\n [ 1.6933564  -1.0674157   0.8876728 ]\n [-1.744577   -0.29134285  0.7646427 ]]", "observation": "[[0.4220477  0.01685923 0.5363604  0.01358718 0.0013675  0.0102217 ]\n [0.4220477  0.01685923 0.5363604  0.01358718 0.0013675  0.0102217 ]\n [0.4220477  0.01685923 0.5363604  0.01358718 0.0013675  0.0102217 ]\n [0.4220477  0.01685923 0.5363604  0.01358718 0.0013675  0.0102217 ]]"}, "_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": "[[-0.13569383  0.03330506  0.18253784]\n [ 0.14352335  0.02696251  0.28770018]\n [ 0.09753665 -0.13780774  0.29847294]\n [ 0.13972832  0.04255902  0.0990178 ]]", "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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -2.627675383863971, "std_reward": 0.545747686730444, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-11T20:48:22.621798"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e0b324d48e2dcdb6bba7a6065ab14e9a2c23b004010ecd19fb7914d20f498c99
+size 2381