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: -2.71 +/- 0.42
+      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 sha256:081e06624e1f4f2f5b4667191b09ee41a9e9dcf62f895e4c930ca6b2c6148b69
+size 108119

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 0x7f597ded95e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f597decee10>"
+    },
+    "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": 1676499294475570296,
+    "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": "[[4.150775e-01 3.113454e-04 5.913712e-01]\n [4.150775e-01 3.113454e-04 5.913712e-01]\n [4.150775e-01 3.113454e-04 5.913712e-01]\n [4.150775e-01 3.113454e-04 5.913712e-01]]",
+        "desired_goal": "[[ 1.1583842   0.47875652  0.6660214 ]\n [ 1.2892867   1.1207045  -0.89759433]\n [-0.926074   -0.25756368 -0.58829194]\n [ 1.1278138  -1.2869345   0.15615904]]",
+        "observation": "[[ 4.1507751e-01  3.1134539e-04  5.9137118e-01 -1.0114732e-02\n  -1.9119198e-03  3.6818106e-04]\n [ 4.1507751e-01  3.1134539e-04  5.9137118e-01 -1.0114732e-02\n  -1.9119198e-03  3.6818106e-04]\n [ 4.1507751e-01  3.1134539e-04  5.9137118e-01 -1.0114732e-02\n  -1.9119198e-03  3.6818106e-04]\n [ 4.1507751e-01  3.1134539e-04  5.9137118e-01 -1.0114732e-02\n  -1.9119198e-03  3.6818106e-04]]"
+    },
+    "_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": "[[-0.08613542  0.12825026  0.25676116]\n [ 0.01379466  0.1360319   0.07739893]\n [-0.07216205 -0.04779879  0.26211998]\n [ 0.14995822  0.06110702  0.04832601]]",
+        "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 sha256:3e19925f49d396741cdfe7e02903620dc3a8436e7d7747e05f6c3d28cb37ac61
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ea1a737c6e66e88a3977e7851648dcddc18f57f879109b13a339044afc869f11
+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 0x7f597ded95e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f597decee10>"}, "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": 1676499294475570296, "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:": "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", "achieved_goal": "[[4.150775e-01 3.113454e-04 5.913712e-01]\n [4.150775e-01 3.113454e-04 5.913712e-01]\n [4.150775e-01 3.113454e-04 5.913712e-01]\n [4.150775e-01 3.113454e-04 5.913712e-01]]", "desired_goal": "[[ 1.1583842   0.47875652  0.6660214 ]\n [ 1.2892867   1.1207045  -0.89759433]\n [-0.926074   -0.25756368 -0.58829194]\n [ 1.1278138  -1.2869345   0.15615904]]", "observation": "[[ 4.1507751e-01  3.1134539e-04  5.9137118e-01 -1.0114732e-02\n  -1.9119198e-03  3.6818106e-04]\n [ 4.1507751e-01  3.1134539e-04  5.9137118e-01 -1.0114732e-02\n  -1.9119198e-03  3.6818106e-04]\n [ 4.1507751e-01  3.1134539e-04  5.9137118e-01 -1.0114732e-02\n  -1.9119198e-03  3.6818106e-04]\n [ 4.1507751e-01  3.1134539e-04  5.9137118e-01 -1.0114732e-02\n  -1.9119198e-03  3.6818106e-04]]"}, "_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": "[[-0.08613542  0.12825026  0.25676116]\n [ 0.01379466  0.1360319   0.07739893]\n [-0.07216205 -0.04779879  0.26211998]\n [ 0.14995822  0.06110702  0.04832601]]", "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": -2.709891414269805, "std_reward": 0.4165348060684552, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-15T23:11:08.389501"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:03a158fccaef3c238b7595dd4cb017cd7b862f039acaa279b35536c539213997
+size 3056