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: -7.25 +/- 1.38
+      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 sha256:3fe4f4e14b89c7a6e8855ce309f16afb8cdb68a4e6c4eaed666b6b8e2405b24a
+size 108100

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 0x7f18f5fec820>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f18f5fe6d80>"
+    },
+    "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": 1679303572198436788,
+    "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": "[[0.36897764 0.00798655 0.4443535 ]\n [0.36897764 0.00798655 0.4443535 ]\n [0.36897764 0.00798655 0.4443535 ]\n [0.36897764 0.00798655 0.4443535 ]]",
+        "desired_goal": "[[-0.9247298  -1.2066896   0.98320794]\n [-0.8972711   1.0576363  -0.7840662 ]\n [ 1.0984797  -0.52375156 -0.43564782]\n [ 1.0859103  -1.2752429   0.365752  ]]",
+        "observation": "[[ 3.6897764e-01  7.9865493e-03  4.4435349e-01 -2.6662733e-02\n   2.9163996e-03 -1.8105107e-04]\n [ 3.6897764e-01  7.9865493e-03  4.4435349e-01 -2.6662733e-02\n   2.9163996e-03 -1.8105107e-04]\n [ 3.6897764e-01  7.9865493e-03  4.4435349e-01 -2.6662733e-02\n   2.9163996e-03 -1.8105107e-04]\n [ 3.6897764e-01  7.9865493e-03  4.4435349e-01 -2.6662733e-02\n   2.9163996e-03 -1.8105107e-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": "[[-0.09970743 -0.01420334  0.26956698]\n [ 0.13060375  0.11146177  0.20426106]\n [-0.04237287  0.11547469  0.03064999]\n [ 0.14652924 -0.1483544   0.28530997]]",
+        "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:aa03f43e63e63b7f1d5a216c43b987ee605f2e49a8e180bec5013574cf1a4cd4
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9b8c5a1ec829f2cada299693f26bf1a922e70e5e0cb09bb0dcfb681f87f48d29
+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 0x7f18f5fec820>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f18f5fe6d80>"}, "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": 1679303572198436788, "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": "[[0.36897764 0.00798655 0.4443535 ]\n [0.36897764 0.00798655 0.4443535 ]\n [0.36897764 0.00798655 0.4443535 ]\n [0.36897764 0.00798655 0.4443535 ]]", "desired_goal": "[[-0.9247298  -1.2066896   0.98320794]\n [-0.8972711   1.0576363  -0.7840662 ]\n [ 1.0984797  -0.52375156 -0.43564782]\n [ 1.0859103  -1.2752429   0.365752  ]]", "observation": "[[ 3.6897764e-01  7.9865493e-03  4.4435349e-01 -2.6662733e-02\n   2.9163996e-03 -1.8105107e-04]\n [ 3.6897764e-01  7.9865493e-03  4.4435349e-01 -2.6662733e-02\n   2.9163996e-03 -1.8105107e-04]\n [ 3.6897764e-01  7.9865493e-03  4.4435349e-01 -2.6662733e-02\n   2.9163996e-03 -1.8105107e-04]\n [ 3.6897764e-01  7.9865493e-03  4.4435349e-01 -2.6662733e-02\n   2.9163996e-03 -1.8105107e-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": "[[-0.09970743 -0.01420334  0.26956698]\n [ 0.13060375  0.11146177  0.20426106]\n [-0.04237287  0.11547469  0.03064999]\n [ 0.14652924 -0.1483544   0.28530997]]", "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-glibc2.31 # 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": -7.25492624938488, "std_reward": 1.379393504643464, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-20T10:02:54.506241"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:447fb2b760d780ad268e35ea4c3a5bf0fdca66423a6332c033b84fa80ce5da79
+size 3056