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: -3.50 +/- 0.52
+      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:d49ded6e51770b0cb47b056040662697c21858f4e33dfe70e52c18292e67eb0c
+size 108044

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 0x7ff61ee96c10>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7ff61ee980c0>"
+    },
+    "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": 1647696,
+    "_total_timesteps": 3000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1680423642372618719,
+    "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": "[[-0.8162464   0.26212808 -0.25017944]\n [ 0.7492866   0.47157374 -0.53938967]\n [ 1.0300364  -0.427515   -0.12818404]\n [ 0.8922054  -0.8337543  -0.74813473]]",
+        "desired_goal": "[[-0.95564497  0.83963567  0.39977056]\n [ 0.35775787  1.2786754   0.53916675]\n [ 0.78418577  0.25144303  0.9562316 ]\n [ 1.1060876  -0.659772   -0.86195165]]",
+        "observation": "[[-0.8162464   0.26212808 -0.25017944  0.5925833  -1.6355833   0.22263257]\n [ 0.7492866   0.47157374 -0.53938967  0.34077674 -0.03586442  0.87229043]\n [ 1.0300364  -0.427515   -0.12818404 -0.19750173 -0.22511837  0.7328216 ]\n [ 0.8922054  -0.8337543  -0.74813473 -0.99317837  0.30996087 -1.6203489 ]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.00056636  0.11075024  0.08759071]\n [ 0.11931164 -0.14591056  0.0484355 ]\n [-0.1347285   0.00726966  0.18561448]\n [ 0.14774886  0.12937579  0.19162884]]",
+        "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": 0.45077333333333336,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 82384,
+    "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 sha256:436a36b20f24fa2737342f16052124d3a58738d120441f4cc8bbad5023b72aab
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e3ac399f1fe84f4d043d2537dfde7c95a28dba88956ee64d9f98124e4218201
+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 0x7ff61ee96c10>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ff61ee980c0>"}, "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": 1647696, "_total_timesteps": 3000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1680423642372618719, "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": "[[-0.8162464   0.26212808 -0.25017944]\n [ 0.7492866   0.47157374 -0.53938967]\n [ 1.0300364  -0.427515   -0.12818404]\n [ 0.8922054  -0.8337543  -0.74813473]]", "desired_goal": "[[-0.95564497  0.83963567  0.39977056]\n [ 0.35775787  1.2786754   0.53916675]\n [ 0.78418577  0.25144303  0.9562316 ]\n [ 1.1060876  -0.659772   -0.86195165]]", "observation": "[[-0.8162464   0.26212808 -0.25017944  0.5925833  -1.6355833   0.22263257]\n [ 0.7492866   0.47157374 -0.53938967  0.34077674 -0.03586442  0.87229043]\n [ 1.0300364  -0.427515   -0.12818404 -0.19750173 -0.22511837  0.7328216 ]\n [ 0.8922054  -0.8337543  -0.74813473 -0.99317837  0.30996087 -1.6203489 ]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.00056636  0.11075024  0.08759071]\n [ 0.11931164 -0.14591056  0.0484355 ]\n [-0.1347285   0.00726966  0.18561448]\n [ 0.14774886  0.12937579  0.19162884]]", "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": 0.45077333333333336, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 82384, "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": -3.4971293016336857, "std_reward": 0.5240060991480168, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-02T09:34:05.789036"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fec0ed1b700a0ea542bb6bcb2ceb0a67202209768fe5abdd181e5238d7188d85
+size 3056