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: -2.92 +/- 0.92
+      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:cb3d6285c999701bafab2b398335c8775c2c87a568cc946b10e2b014f555862f
+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 0x7f885c798dc0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f885c797480>"
+    },
+    "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": 1675637544729642610,
+    "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": "[[ 0.3906241  -0.02191113  0.5216492 ]\n [ 0.3906241  -0.02191113  0.5216492 ]\n [ 0.3906241  -0.02191113  0.5216492 ]\n [ 0.3906241  -0.02191113  0.5216492 ]]",
+        "desired_goal": "[[ 0.9839848  -1.6990985   0.40375718]\n [-0.15541999 -1.1929824   1.3344681 ]\n [-1.1945136  -1.033481    0.64051574]\n [ 0.921189   -0.25694507  1.0923162 ]]",
+        "observation": "[[ 0.3906241  -0.02191113  0.5216492  -0.01222556 -0.00257167 -0.00857959]\n [ 0.3906241  -0.02191113  0.5216492  -0.01222556 -0.00257167 -0.00857959]\n [ 0.3906241  -0.02191113  0.5216492  -0.01222556 -0.00257167 -0.00857959]\n [ 0.3906241  -0.02191113  0.5216492  -0.01222556 -0.00257167 -0.00857959]]"
+    },
+    "_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": "[[ 0.13778439 -0.02178581  0.02369366]\n [-0.07284086  0.07716347  0.24153747]\n [ 0.04901032  0.04342033  0.08685933]\n [ 0.1107906  -0.12734851  0.07296262]]",
+        "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 sha256:7daed73769c3a5628c464939133fa765a1f6ab9488c7cc05b9ecb92bba6daf1d
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fe52560e286963a289f4b35db5b964bf52890fa7b049c698edf6e2074e2d6bb3
+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 0x7f885c798dc0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f885c797480>"}, "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": 1675637544729642610, "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": "[[ 0.3906241  -0.02191113  0.5216492 ]\n [ 0.3906241  -0.02191113  0.5216492 ]\n [ 0.3906241  -0.02191113  0.5216492 ]\n [ 0.3906241  -0.02191113  0.5216492 ]]", "desired_goal": "[[ 0.9839848  -1.6990985   0.40375718]\n [-0.15541999 -1.1929824   1.3344681 ]\n [-1.1945136  -1.033481    0.64051574]\n [ 0.921189   -0.25694507  1.0923162 ]]", "observation": "[[ 0.3906241  -0.02191113  0.5216492  -0.01222556 -0.00257167 -0.00857959]\n [ 0.3906241  -0.02191113  0.5216492  -0.01222556 -0.00257167 -0.00857959]\n [ 0.3906241  -0.02191113  0.5216492  -0.01222556 -0.00257167 -0.00857959]\n [ 0.3906241  -0.02191113  0.5216492  -0.01222556 -0.00257167 -0.00857959]]"}, "_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": "[[ 0.13778439 -0.02178581  0.02369366]\n [-0.07284086  0.07716347  0.24153747]\n [ 0.04901032  0.04342033  0.08685933]\n [ 0.1107906  -0.12734851  0.07296262]]", "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": -2.9221412201877683, "std_reward": 0.9218037109895775, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-05T23:34:32.160872"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:49512a186a9410a4852de07ae13212478fd77f5284428fbe1b6f85ea48cea2a4
+size 3056