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: -6.39 +/- 1.80
+      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:f508ea579b6bb881bb02f7516e109acc3b36380019614796ebaa46f6d21344eb
+size 108037

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 0x784a21a7ea70>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x784a21a81980>"
+    },
+    "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": 1690389609345654583,
+    "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": "[[0.5026276  0.01955783 0.3773906 ]\n [0.5026276  0.01955783 0.3773906 ]\n [0.5026276  0.01955783 0.3773906 ]\n [0.5026276  0.01955783 0.3773906 ]]",
+        "desired_goal": "[[-0.2218783  -0.70064396  0.29441333]\n [-0.59549993 -0.26461506 -0.98061556]\n [-1.5939629  -0.8156005   1.4998511 ]\n [ 0.6309037  -0.5915716  -1.086986  ]]",
+        "observation": "[[0.5026276  0.01955783 0.3773906  0.03524334 0.00468668 0.02416368]\n [0.5026276  0.01955783 0.3773906  0.03524334 0.00468668 0.02416368]\n [0.5026276  0.01955783 0.3773906  0.03524334 0.00468668 0.02416368]\n [0.5026276  0.01955783 0.3773906  0.03524334 0.00468668 0.02416368]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAaBPUPV5p7z34OAo+V9dpPL2oJL1GiD499qaeveKFlL2BMTc+coqcvUhCuryDlt89lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAACUaA5LBEsGhpRoEnSUUpR1Lg==",
+        "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": "[[ 0.10355264  0.11690019  0.13498294]\n [ 0.01427253 -0.04019998  0.04651668]\n [-0.07746689 -0.07252099  0.17889978]\n [-0.07643594 -0.02273668  0.1091738 ]]",
+        "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 sha256:6b0a817333b726449037a64ea4b6e7cff86dd610ab542820500df45ed3ac0b73
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:762a4bfa53792d477b42c284b04004afbe130919f0a2de2ba3a6c506071c33b5
+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-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.6
+- 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 0x784a21a7ea70>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x784a21a81980>"}, "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": 1690389609345654583, "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": "[[0.5026276  0.01955783 0.3773906 ]\n [0.5026276  0.01955783 0.3773906 ]\n [0.5026276  0.01955783 0.3773906 ]\n [0.5026276  0.01955783 0.3773906 ]]", "desired_goal": "[[-0.2218783  -0.70064396  0.29441333]\n [-0.59549993 -0.26461506 -0.98061556]\n [-1.5939629  -0.8156005   1.4998511 ]\n [ 0.6309037  -0.5915716  -1.086986  ]]", "observation": "[[0.5026276  0.01955783 0.3773906  0.03524334 0.00468668 0.02416368]\n [0.5026276  0.01955783 0.3773906  0.03524334 0.00468668 0.02416368]\n [0.5026276  0.01955783 0.3773906  0.03524334 0.00468668 0.02416368]\n [0.5026276  0.01955783 0.3773906  0.03524334 0.00468668 0.02416368]]"}, "_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": "[[ 0.10355264  0.11690019  0.13498294]\n [ 0.01427253 -0.04019998  0.04651668]\n [-0.07746689 -0.07252099  0.17889978]\n [-0.07643594 -0.02273668  0.1091738 ]]", "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, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "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": -6.3891371646896005, "std_reward": 1.801245590659014, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-26T17:42:13.341138"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:928707af814d0061528dddad4ef4c602ea9936880ecd650364f62a17cbad2bdd
+size 2387