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: -1.83 +/- 0.30
+      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 sha256:329b44d44fca768abb3e2e321fb25694d87b03fbf09eabda2eb236e1c15248a9
+size 108027

a2c-PandaReachDense-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

a2c-PandaReachDense-v2/data

@@ -0,0 +1,94 @@
+{
+    "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 0x7f50806af820>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f50806aea40>"
+    },
+    "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
+        }
+    },
+    "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:": "gAWVbQEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLA4WUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaApLA4WUjAFDlHSUUpSMBGhpZ2iUaBIolgwAAAAAAAAAAACAPwAAgD8AAIA/lGgKSwOFlGgVdJRSlIwNYm91bmRlZF9iZWxvd5RoEiiWAwAAAAAAAAABAQGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYDAAAAAAAAAAEBAZRoIUsDhZRoFXSUUpSMCl9ucF9yYW5kb22UTnViLg==",
+        "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,
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1680804719098147411,
+    "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": "[[ 0.22209132 -0.03705206  0.44531736]\n [ 0.22209132 -0.03705206  0.44531736]\n [ 0.22209132 -0.03705206  0.44531736]\n [ 0.22209132 -0.03705206  0.44531736]]",
+        "desired_goal": "[[ 0.05219232  1.4103696  -0.68158317]\n [ 0.85336614 -0.72799695  1.4527907 ]\n [ 1.7029498   1.2178347   1.1023922 ]\n [-0.01914161 -0.92386097 -0.48886067]]",
+        "observation": "[[ 0.22209132 -0.03705206  0.44531736  0.01577636 -0.00538643  0.00882086]\n [ 0.22209132 -0.03705206  0.44531736  0.01577636 -0.00538643  0.00882086]\n [ 0.22209132 -0.03705206  0.44531736  0.01577636 -0.00538643  0.00882086]\n [ 0.22209132 -0.03705206  0.44531736  0.01577636 -0.00538643  0.00882086]]"
+    },
+    "_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": "[[-0.05988585  0.12826657  0.26247245]\n [ 0.06827625 -0.00581589  0.17303257]\n [ 0.14251603 -0.11253089  0.05478824]\n [-0.05113593  0.03933192  0.17574318]]",
+        "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,
+    "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
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a5599195b387e020d0a7ac7feb22f7c5d3363328bac43c2ddca2702e72c3b121
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:01f20c1b5881145b1c90c125f36016f6d4194c910348a1ff7fc106fad646fab4
+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: 1.7.0
+- 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 0x7f50806af820>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f50806aea40>"}, "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}}, "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, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1680804719098147411, "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": "[[ 0.22209132 -0.03705206  0.44531736]\n [ 0.22209132 -0.03705206  0.44531736]\n [ 0.22209132 -0.03705206  0.44531736]\n [ 0.22209132 -0.03705206  0.44531736]]", "desired_goal": "[[ 0.05219232  1.4103696  -0.68158317]\n [ 0.85336614 -0.72799695  1.4527907 ]\n [ 1.7029498   1.2178347   1.1023922 ]\n [-0.01914161 -0.92386097 -0.48886067]]", "observation": "[[ 0.22209132 -0.03705206  0.44531736  0.01577636 -0.00538643  0.00882086]\n [ 0.22209132 -0.03705206  0.44531736  0.01577636 -0.00538643  0.00882086]\n [ 0.22209132 -0.03705206  0.44531736  0.01577636 -0.00538643  0.00882086]\n [ 0.22209132 -0.03705206  0.44531736  0.01577636 -0.00538643  0.00882086]]"}, "_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": "[[-0.05988585  0.12826657  0.26247245]\n [ 0.06827625 -0.00581589  0.17303257]\n [ 0.14251603 -0.11253089  0.05478824]\n [-0.05113593  0.03933192  0.17574318]]", "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, "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, "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": "1.7.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -1.8250534237362444, "std_reward": 0.29865478636811643, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-06T19:01:27.482869"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8227402ad7671493c9f410132dd7d8d6e54e9601aa609c0323634666dba1114e
+size 3056