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: -6.80 +/- 1.55
+      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:e58b55b0484c677f657c8ee5f711a5799d8cf2f5cb9a79e7aa00eeda420f2fbb
+size 108003

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 0x7fca844414c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fca8443fa00>"
+    },
+    "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": 10000000,
+    "_total_timesteps": 10000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1679446606135447005,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWV1QIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMUS9vcHQvY29uZGEvZW52cy9tbDIvbGliL3B5dGhvbjMuOS9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMUS9vcHQvY29uZGEvZW52cy9tbDIvbGliL3B5dGhvbjMuOS9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/RvAGjbi6x4WUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAhwwAP56rFz73Vws/hwwAP56rFz73Vws/hwwAP56rFz73Vws/hwwAP56rFz73Vws/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAul46v/YWsD8zQly/CZOJP605mr6EubA/cQptv5AFgL688Ca/ysJVPx3BSj9qhnY/lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAACHDAA/nqsXPvdXCz9Kaqg9sogAvOubiT2HDAA/nqsXPvdXCz9Kaqg9sogAvOubiT2HDAA/nqsXPvdXCz9Kaqg9sogAvOubiT2HDAA/nqsXPvdXCz9Kaqg9sogAvOubiT2UaA5LBEsGhpRoEnSUUpR1Lg==",
+        "achieved_goal": "[[0.50019115 0.1481156  0.544311  ]\n [0.50019115 0.1481156  0.544311  ]\n [0.50019115 0.1481156  0.544311  ]\n [0.50019115 0.1481156  0.544311  ]]",
+        "desired_goal": "[[-0.7280079   1.3757007  -0.8603851 ]\n [ 1.0747997  -0.30122128  1.3806615 ]\n [-0.9259406  -0.25004244 -0.6521108 ]\n [ 0.8350035   0.7920092   0.9629885 ]]",
+        "observation": "[[ 0.50019115  0.1481156   0.544311    0.08223398 -0.00784509  0.06719192]\n [ 0.50019115  0.1481156   0.544311    0.08223398 -0.00784509  0.06719192]\n [ 0.50019115  0.1481156   0.544311    0.08223398 -0.00784509  0.06719192]\n [ 0.50019115  0.1481156   0.544311    0.08223398 -0.00784509  0.06719192]]"
+    },
+    "_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": "[[ 0.07145338  0.05836111  0.14016089]\n [ 0.13778877 -0.09443574  0.23761822]\n [ 0.02942472 -0.04915717  0.14464375]\n [-0.02813132  0.0755335   0.17513838]]",
+        "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": 500000,
+    "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 sha256:0d58d4beda536a471c4cf0aef7251d9f426ad98054a233e104e702620a188654
+size 44670

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4046723c8eaee1854c730b01bb92ab29c5a5aaa160276d9c1780cbdd054eff72
+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-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 # 1 SMP Fri Jan 27 02:56:13 UTC 2023
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.11.0+cu115
+- GPU Enabled: True
+- Numpy: 1.21.2
+- 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 0x7fca844414c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fca8443fa00>"}, "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": 10000000, "_total_timesteps": 10000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1679446606135447005, "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": "[[0.50019115 0.1481156  0.544311  ]\n [0.50019115 0.1481156  0.544311  ]\n [0.50019115 0.1481156  0.544311  ]\n [0.50019115 0.1481156  0.544311  ]]", "desired_goal": "[[-0.7280079   1.3757007  -0.8603851 ]\n [ 1.0747997  -0.30122128  1.3806615 ]\n [-0.9259406  -0.25004244 -0.6521108 ]\n [ 0.8350035   0.7920092   0.9629885 ]]", "observation": "[[ 0.50019115  0.1481156   0.544311    0.08223398 -0.00784509  0.06719192]\n [ 0.50019115  0.1481156   0.544311    0.08223398 -0.00784509  0.06719192]\n [ 0.50019115  0.1481156   0.544311    0.08223398 -0.00784509  0.06719192]\n [ 0.50019115  0.1481156   0.544311    0.08223398 -0.00784509  0.06719192]]"}, "_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": "[[ 0.07145338  0.05836111  0.14016089]\n [ 0.13778877 -0.09443574  0.23761822]\n [ 0.02942472 -0.04915717  0.14464375]\n [-0.02813132  0.0755335   0.17513838]]", "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": 500000, "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-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.31 # 1 SMP Fri Jan 27 02:56:13 UTC 2023", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.11.0+cu115", "GPU Enabled": "True", "Numpy": "1.21.2", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -6.799389395490289, "std_reward": 1.5496430895926816, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-22T07:05:55.759357"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d4cbbfc835b95db9494b5a26662d7ca0a73d8da75407e28be310e9fc30f32f41
+size 3056