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: -2.68 +/- 0.53
+      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 sha256:0f2ed66b27bf13fe14414684c371fb75589b1d2264928bb91e614ea7f87fa791
+size 108062

a2c-PandaReachDense-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

a2c-PandaReachDense-v2/data

@@ -0,0 +1,95 @@
+{
+    "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 0x78f1812fe170>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x78f1814f2f80>"
+    },
+    "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
+        }
+    },
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1689457199481833051,
+    "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": "[[0.30857575 0.03786641 0.51364696]\n [0.30857575 0.03786641 0.51364696]\n [0.30857575 0.03786641 0.51364696]\n [0.30857575 0.03786641 0.51364696]]",
+        "desired_goal": "[[-1.5935457   1.0067593  -0.28899118]\n [ 1.541712    0.6662396  -1.0864208 ]\n [-1.6343299  -0.51804245  0.53037584]\n [ 1.4363731   1.4664496   0.597247  ]]",
+        "observation": "[[ 0.30857575  0.03786641  0.51364696 -0.00736497  0.00748425 -0.00828825]\n [ 0.30857575  0.03786641  0.51364696 -0.00736497  0.00748425 -0.00828825]\n [ 0.30857575  0.03786641  0.51364696 -0.00736497  0.00748425 -0.00828825]\n [ 0.30857575  0.03786641  0.51364696 -0.00736497  0.00748425 -0.00828825]]"
+    },
+    "_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": "[[ 0.09904689 -0.09722251  0.13574378]\n [ 0.02324818  0.10534827  0.2854683 ]\n [-0.05519595 -0.03369405  0.29612723]\n [-0.02974569  0.14563125  0.14796159]]",
+        "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,
+    "_stats_window_size": 100,
+    "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,
+    "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
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8566f267b5328f54e923cfc36a2a51193043445150fe70c6d742b63bba8803c5
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:180b27b86a79e2c78c721de119ffdb0f4196f3cc269aa2fa745ee52f61c3ba22
+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-5.15.109+-x86_64-with-glibc2.31 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.1+cu118
+- 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 0x78f1812fe170>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x78f1814f2f80>"}, "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}}, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1689457199481833051, "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": "[[0.30857575 0.03786641 0.51364696]\n [0.30857575 0.03786641 0.51364696]\n [0.30857575 0.03786641 0.51364696]\n [0.30857575 0.03786641 0.51364696]]", "desired_goal": "[[-1.5935457   1.0067593  -0.28899118]\n [ 1.541712    0.6662396  -1.0864208 ]\n [-1.6343299  -0.51804245  0.53037584]\n [ 1.4363731   1.4664496   0.597247  ]]", "observation": "[[ 0.30857575  0.03786641  0.51364696 -0.00736497  0.00748425 -0.00828825]\n [ 0.30857575  0.03786641  0.51364696 -0.00736497  0.00748425 -0.00828825]\n [ 0.30857575  0.03786641  0.51364696 -0.00736497  0.00748425 -0.00828825]\n [ 0.30857575  0.03786641  0.51364696 -0.00736497  0.00748425 -0.00828825]]"}, "_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": "[[ 0.09904689 -0.09722251  0.13574378]\n [ 0.02324818  0.10534827  0.2854683 ]\n [-0.05519595 -0.03369405  0.29612723]\n [-0.02974569  0.14563125  0.14796159]]", "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, "_stats_window_size": 100, "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, "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, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.31 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -2.6768122875131666, "std_reward": 0.5266521083506096, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-15T22:28:41.103580"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:98346f0f7d54f006bee9d992533709372da4f30e9a1b7cd7aa95b590c273829e
+size 2387