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: -1.33 +/- 0.48
+      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:f8ce34bb897c0c676cd92733f2da6deaff387078fb2cd127335003e22026ad53
+size 108074

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 0x7fc693291e10>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fc693297680>"
+    },
+    "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": 1691167420296638345,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAS13UPkVpHL3Wqgs/S13UPkVpHL3Wqgs/S13UPkVpHL3Wqgs/S13UPkVpHL3Wqgs/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAA/hEIv0mrkb83+iO9723JvakSOD904KG/VTqgv6cVFb/U47W//m3DP7X1RT8izIS/lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAABLXdQ+RWkcvdaqCz+wW4I8kupJu+FUjjtLXdQ+RWkcvdaqCz+wW4I8kupJu+FUjjtLXdQ+RWkcvdaqCz+wW4I8kupJu+FUjjtLXdQ+RWkcvdaqCz+wW4I8kupJu+FUjjuUaA5LBEsGhpRoEnSUUpR1Lg==",
+        "achieved_goal": "[[ 0.41477427 -0.03818633  0.5455755 ]\n [ 0.41477427 -0.03818633  0.5455755 ]\n [ 0.41477427 -0.03818633  0.5455755 ]\n [ 0.41477427 -0.03818633  0.5455755 ]]",
+        "desired_goal": "[[-0.53152454 -1.1380397  -0.04003355]\n [-0.09835421  0.71903473 -1.2646623 ]\n [-1.2517802  -0.58236164 -1.4210153 ]\n [ 1.5267942   0.77328044 -1.0374796 ]]",
+        "observation": "[[ 0.41477427 -0.03818633  0.5455755   0.01591286 -0.003081    0.00434361]\n [ 0.41477427 -0.03818633  0.5455755   0.01591286 -0.003081    0.00434361]\n [ 0.41477427 -0.03818633  0.5455755   0.01591286 -0.003081    0.00434361]\n [ 0.41477427 -0.03818633  0.5455755   0.01591286 -0.003081    0.00434361]]"
+    },
+    "_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": "[[-0.10212265 -0.03678404  0.21939501]\n [-0.01352911  0.14449881  0.07815792]\n [-0.0240276   0.1158774   0.27979034]\n [-0.05695412  0.05074753  0.05003119]]",
+        "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 sha256:7c2b7e325de59197dd9c55b2df8550702aa5a035ef1d5e1a1893861a5dcdbde3
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2640fbceb4bf1ab6bf09bbcff95c476f0b3d35a8c0b77bfd9250ae563168c863
+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: 3.10.12
+- 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 0x7fc693291e10>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fc693297680>"}, "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": 1691167420296638345, "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": "[[ 0.41477427 -0.03818633  0.5455755 ]\n [ 0.41477427 -0.03818633  0.5455755 ]\n [ 0.41477427 -0.03818633  0.5455755 ]\n [ 0.41477427 -0.03818633  0.5455755 ]]", "desired_goal": "[[-0.53152454 -1.1380397  -0.04003355]\n [-0.09835421  0.71903473 -1.2646623 ]\n [-1.2517802  -0.58236164 -1.4210153 ]\n [ 1.5267942   0.77328044 -1.0374796 ]]", "observation": "[[ 0.41477427 -0.03818633  0.5455755   0.01591286 -0.003081    0.00434361]\n [ 0.41477427 -0.03818633  0.5455755   0.01591286 -0.003081    0.00434361]\n [ 0.41477427 -0.03818633  0.5455755   0.01591286 -0.003081    0.00434361]\n [ 0.41477427 -0.03818633  0.5455755   0.01591286 -0.003081    0.00434361]]"}, "_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": "[[-0.10212265 -0.03678404  0.21939501]\n [-0.01352911  0.14449881  0.07815792]\n [-0.0240276   0.1158774   0.27979034]\n [-0.05695412  0.05074753  0.05003119]]", "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-glibc2.35 # 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": -1.3340500927064567, "std_reward": 0.4831890937175408, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-04T17:47:25.787943"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d18f857b2f9be4ee9867dcd01f6baa4bf17bd16678df68282394737cda66e88a
+size 2387