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: -2.97 +/- 1.73
+      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 sha256:3591a743c314037a7f30c671779c84c694a0ac1ad4ccbaf65f6c588d8ca5065c
+size 108014

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 0x7fb30bf00700>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fb30befc840>"
+    },
+    "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:": "gAWVbQEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLA4WUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaApLA4WUjAFDlHSUUpSMBGhpZ2iUaBIolgwAAAAAAAAAAACAPwAAgD8AAIA/lGgKSwOFlGgVdJRSlIwNYm91bmRlZF9iZWxvd5RoEiiWAwAAAAAAAAABAQGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYDAAAAAAAAAAEBAZRoIUsDhZRoFXSUUpSMCl9ucF9yYW5kb22UTnViLg==",
+        "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": 400000,
+    "_total_timesteps": 400000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1680311635233362920,
+    "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": "[[ 0.2555436 -0.0690699  0.5420358]\n [ 0.2555436 -0.0690699  0.5420358]\n [ 0.2555436 -0.0690699  0.5420358]\n [ 0.2555436 -0.0690699  0.5420358]]",
+        "desired_goal": "[[ 0.476921   -0.62591773  0.56805843]\n [ 1.2266408  -1.3969105  -0.7599364 ]\n [ 1.6216655  -0.7446667  -0.21231575]\n [-1.1825602  -1.5202237   1.240441  ]]",
+        "observation": "[[ 0.2555436  -0.0690699   0.5420358  -0.00732838 -0.00362855  0.00406719]\n [ 0.2555436  -0.0690699   0.5420358  -0.00732838 -0.00362855  0.00406719]\n [ 0.2555436  -0.0690699   0.5420358  -0.00732838 -0.00362855  0.00406719]\n [ 0.2555436  -0.0690699   0.5420358  -0.00732838 -0.00362855  0.00406719]]"
+    },
+    "_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": "[[-0.12012471 -0.05892724  0.10607412]\n [-0.08486774 -0.13732317  0.01989922]\n [-0.01140505  0.05670216  0.029443  ]\n [-0.10342527 -0.13273537  0.19501004]]",
+        "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": 20000,
+    "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:da6247b624fb12482fcf81e0877944d3e269631ccc8d4d47d542369816e0e20c
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5cfc0bf63bdfcb2aadfce5af0d086c7e1b33b699a822ebb12792f9d603801515
+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 0x7fb30bf00700>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fb30befc840>"}, "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": 400000, "_total_timesteps": 400000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1680311635233362920, "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": "[[ 0.2555436 -0.0690699  0.5420358]\n [ 0.2555436 -0.0690699  0.5420358]\n [ 0.2555436 -0.0690699  0.5420358]\n [ 0.2555436 -0.0690699  0.5420358]]", "desired_goal": "[[ 0.476921   -0.62591773  0.56805843]\n [ 1.2266408  -1.3969105  -0.7599364 ]\n [ 1.6216655  -0.7446667  -0.21231575]\n [-1.1825602  -1.5202237   1.240441  ]]", "observation": "[[ 0.2555436  -0.0690699   0.5420358  -0.00732838 -0.00362855  0.00406719]\n [ 0.2555436  -0.0690699   0.5420358  -0.00732838 -0.00362855  0.00406719]\n [ 0.2555436  -0.0690699   0.5420358  -0.00732838 -0.00362855  0.00406719]\n [ 0.2555436  -0.0690699   0.5420358  -0.00732838 -0.00362855  0.00406719]]"}, "_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": "[[-0.12012471 -0.05892724  0.10607412]\n [-0.08486774 -0.13732317  0.01989922]\n [-0.01140505  0.05670216  0.029443  ]\n [-0.10342527 -0.13273537  0.19501004]]", "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": 20000, "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": -2.9670771904289723, "std_reward": 1.7329098703449668, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-01T01:36:11.925093"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:60e823d694bc85b4b35acf09cc57f17bc9ef6b9d339451512b08d7b6935617e4
+size 3056