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: -31.44 +/- 15.98
+      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:ea02a1eafba740607337b8e005a01a3af291f07d9901d1ea27917161b476242f
+size 108039

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 0x7fcc09d75480>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fcc09d67300>"
+    },
+    "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": 1685895848398568380,
+    "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": "[[0.30566072 0.01098149 0.52495706]\n [0.30566072 0.01098149 0.52495706]\n [0.30566072 0.01098149 0.52495706]\n [0.30566072 0.01098149 0.52495706]]",
+        "desired_goal": "[[-1.5434109  -0.62748396 -1.246477  ]\n [ 1.7249053   0.16003197  1.3325373 ]\n [ 0.14870712 -0.66131675  1.2970759 ]\n [ 0.3322922  -0.69798315  0.5925578 ]]",
+        "observation": "[[0.30566072 0.01098149 0.52495706 0.02707379 0.00204781 0.00445078]\n [0.30566072 0.01098149 0.52495706 0.02707379 0.00204781 0.00445078]\n [0.30566072 0.01098149 0.52495706 0.02707379 0.00204781 0.00445078]\n [0.30566072 0.01098149 0.52495706 0.02707379 0.00204781 0.00445078]]"
+    },
+    "_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": "[[ 0.07393055 -0.03013764  0.11148222]\n [-0.03380796  0.025739    0.04321813]\n [ 0.02565662  0.09575387  0.03534955]\n [-0.11646619  0.13073158  0.21234877]]",
+        "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:": "gAWVcwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLA4WUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaAtLA4WUjAFDlHSUUpSMBGhpZ2iUaBMolgwAAAAAAAAAAACAPwAAgD8AAIA/lGgLSwOFlGgWdJRSlIwNYm91bmRlZF9iZWxvd5RoEyiWAwAAAAAAAAABAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUaBZ0lFKUjA1ib3VuZGVkX2Fib3ZllGgTKJYDAAAAAAAAAAEBAZRoIksDhZRoFnSUUpSMCl9ucF9yYW5kb22UTnViLg==",
+        "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:c348dd7137c5455c234521422ebe2d7d6670efdf8457f935e1035033ae681f24
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a2c7956825f9b665d228d82749b99e09fad156092b0f5a3c16a664b3bb1627b3
+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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
+- Python: 3.10.11
+- 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 0x7fcc09d75480>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fcc09d67300>"}, "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": 1685895848398568380, "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": "[[0.30566072 0.01098149 0.52495706]\n [0.30566072 0.01098149 0.52495706]\n [0.30566072 0.01098149 0.52495706]\n [0.30566072 0.01098149 0.52495706]]", "desired_goal": "[[-1.5434109  -0.62748396 -1.246477  ]\n [ 1.7249053   0.16003197  1.3325373 ]\n [ 0.14870712 -0.66131675  1.2970759 ]\n [ 0.3322922  -0.69798315  0.5925578 ]]", "observation": "[[0.30566072 0.01098149 0.52495706 0.02707379 0.00204781 0.00445078]\n [0.30566072 0.01098149 0.52495706 0.02707379 0.00204781 0.00445078]\n [0.30566072 0.01098149 0.52495706 0.02707379 0.00204781 0.00445078]\n [0.30566072 0.01098149 0.52495706 0.02707379 0.00204781 0.00445078]]"}, "_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": "[[ 0.07393055 -0.03013764  0.11148222]\n [-0.03380796  0.025739    0.04321813]\n [ 0.02565662  0.09575387  0.03534955]\n [-0.11646619  0.13073158  0.21234877]]", "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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.11", "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": -31.44154452085495, "std_reward": 15.975067580190581, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-06-04T17:12:07.670018"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:294a956dd610f29abb7ca9563f11d90f6660a5a35e46f6e856ae41da2f7b7a4b
+size 2387