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: -9.78 +/- 3.27
+      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:a583fcd29e9b12594c3efccb53610ab9188d7ca13d5cce4a3c16203934da7d1f
+size 108130

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 0x7fba9dbb91f0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fba9dbb7e80>"
+    },
+    "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": 1680981136924972598,
+    "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": "[[0.39652166 0.00413489 0.55917776]\n [0.39652166 0.00413489 0.55917776]\n [0.39652166 0.00413489 0.55917776]\n [0.39652166 0.00413489 0.55917776]]",
+        "desired_goal": "[[ 0.78071606  1.454428   -0.6656821 ]\n [ 1.3902283  -0.988333    0.10677522]\n [ 0.16291812 -0.19079308  1.5785829 ]\n [-0.8957347   1.672223    1.2271863 ]]",
+        "observation": "[[ 3.9652166e-01  4.1348906e-03  5.5917776e-01  1.0820651e-02\n  -3.0212995e-04  1.2877373e-02]\n [ 3.9652166e-01  4.1348906e-03  5.5917776e-01  1.0820651e-02\n  -3.0212995e-04  1.2877373e-02]\n [ 3.9652166e-01  4.1348906e-03  5.5917776e-01  1.0820651e-02\n  -3.0212995e-04  1.2877373e-02]\n [ 3.9652166e-01  4.1348906e-03  5.5917776e-01  1.0820651e-02\n  -3.0212995e-04  1.2877373e-02]]"
+    },
+    "_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": "[[-0.00339267 -0.0612204   0.1605564 ]\n [-0.13396472  0.0527552   0.01724586]\n [-0.09352186  0.03458038  0.05879519]\n [ 0.09372456  0.03136649  0.05293778]]",
+        "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 sha256:8877866e9b71d9e9dc50eafd10b9aaa24eca79a7fda6e6bbb89573d5e0bde990
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:66b60cc82fb775c0fa4d9891185340dc8ea0a4cf4fa19d6fe97586cc8e69c9c1
+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: 1.8.0
+- PyTorch: 2.0.0+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 0x7fba9dbb91f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fba9dbb7e80>"}, "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": 1680981136924972598, "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": "[[0.39652166 0.00413489 0.55917776]\n [0.39652166 0.00413489 0.55917776]\n [0.39652166 0.00413489 0.55917776]\n [0.39652166 0.00413489 0.55917776]]", "desired_goal": "[[ 0.78071606  1.454428   -0.6656821 ]\n [ 1.3902283  -0.988333    0.10677522]\n [ 0.16291812 -0.19079308  1.5785829 ]\n [-0.8957347   1.672223    1.2271863 ]]", "observation": "[[ 3.9652166e-01  4.1348906e-03  5.5917776e-01  1.0820651e-02\n  -3.0212995e-04  1.2877373e-02]\n [ 3.9652166e-01  4.1348906e-03  5.5917776e-01  1.0820651e-02\n  -3.0212995e-04  1.2877373e-02]\n [ 3.9652166e-01  4.1348906e-03  5.5917776e-01  1.0820651e-02\n  -3.0212995e-04  1.2877373e-02]\n [ 3.9652166e-01  4.1348906e-03  5.5917776e-01  1.0820651e-02\n  -3.0212995e-04  1.2877373e-02]]"}, "_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": "[[-0.00339267 -0.0612204   0.1605564 ]\n [-0.13396472  0.0527552   0.01724586]\n [-0.09352186  0.03458038  0.05879519]\n [ 0.09372456  0.03136649  0.05293778]]", "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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -9.784526352584361, "std_reward": 3.268660433819775, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-08T20:03:41.199468"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:48e1815dca5179fe3e47b71312d644e9ee6abbbdb51c8e4e6e6f3c7ee32145f9
+size 2381