Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v3
+      type: PandaReachDense-v3
+    metrics:
+    - type: mean_reward
+      value: -0.18 +/- 0.10
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaReachDense-v3**
+This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
+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-v3.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9144d4ab4a930d7cb3a8f0863591d5af2d0d12429be9a3fe5b815300d84ecd7f
+size 106915

a2c-PandaReachDense-v3/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0

a2c-PandaReachDense-v3/data

@@ -0,0 +1,97 @@
+{
+    "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 0x7bf481ab5630>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7bf481ab10c0>"
+    },
+    "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": 1691782241107070005,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 0.2721773  -0.30372956 -0.12968397]\n [-0.9075418   1.2085209  -0.07316224]\n [ 0.25749737  0.00331645  0.4318083 ]\n [-0.11646221  0.43020877 -0.2132572 ]]",
+        "desired_goal": "[[ 0.4406997  -0.37173155 -0.99078995]\n [-1.3090974   0.52736735  0.1989973 ]\n [ 0.46885312  0.93886644 -0.6653521 ]\n [-0.5139183  -0.11811743 -0.7234395 ]]",
+        "observation": "[[ 2.7217731e-01 -3.0372956e-01 -1.2968397e-01 -1.2285432e-01\n  -4.2980924e-01 -1.3692218e+00]\n [-9.0754181e-01  1.2085209e+00 -7.3162235e-02 -1.1375128e+00\n   6.0034275e-01 -1.2715675e+00]\n [ 2.5749737e-01  3.3164511e-03  4.3180829e-01  4.7351012e-01\n  -8.6995846e-05  3.8321379e-01]\n [-1.1646221e-01  4.3020877e-01 -2.1325719e-01 -1.8532045e+00\n   1.5574485e+00 -1.3953645e+00]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAQCUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.04436199  0.01954167  0.14828378]\n [ 0.07881691  0.03221341  0.11949927]\n [ 0.09388779  0.10907752  0.11800235]\n [ 0.10196719 -0.07044425  0.25962606]]",
+        "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 'gymnasium.spaces.dict.Dict'>",
+        ":serialized:": "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",
+        "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])",
+        "_shape": null,
+        "dtype": null,
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True]",
+        "bounded_above": "[ True  True  True]",
+        "_shape": [
+            3
+        ],
+        "low": "[-1. -1. -1.]",
+        "high": "[1. 1. 1.]",
+        "low_repr": "-1.0",
+        "high_repr": "1.0",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

a2c-PandaReachDense-v3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4e8fa3d2a4c7e078a74ff5058964df4228c5b87854446f9100522ee872f39d7b
+size 44734

a2c-PandaReachDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:108e2ab8057704510f1c73abe687d77c4a934116d2f8e4893438688c8ce0501b
+size 46014

a2c-PandaReachDense-v3/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

a2c-PandaReachDense-v3/system_info.txt

@@ -0,0 +1,9 @@
+- 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: 2.0.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

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 0x7bf481ab5630>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7bf481ab10c0>"}, "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": 1691782241107070005, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 0.2721773  -0.30372956 -0.12968397]\n [-0.9075418   1.2085209  -0.07316224]\n [ 0.25749737  0.00331645  0.4318083 ]\n [-0.11646221  0.43020877 -0.2132572 ]]", "desired_goal": "[[ 0.4406997  -0.37173155 -0.99078995]\n [-1.3090974   0.52736735  0.1989973 ]\n [ 0.46885312  0.93886644 -0.6653521 ]\n [-0.5139183  -0.11811743 -0.7234395 ]]", "observation": "[[ 2.7217731e-01 -3.0372956e-01 -1.2968397e-01 -1.2285432e-01\n  -4.2980924e-01 -1.3692218e+00]\n [-9.0754181e-01  1.2085209e+00 -7.3162235e-02 -1.1375128e+00\n   6.0034275e-01 -1.2715675e+00]\n [ 2.5749737e-01  3.3164511e-03  4.3180829e-01  4.7351012e-01\n  -8.6995846e-05  3.8321379e-01]\n [-1.1646221e-01  4.3020877e-01 -2.1325719e-01 -1.8532045e+00\n   1.5574485e+00 -1.3953645e+00]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAQCUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.04436199  0.01954167  0.14828378]\n [ 0.07881691  0.03221341  0.11949927]\n [ 0.09388779  0.10907752  0.11800235]\n [ 0.10196719 -0.07044425  0.25962606]]", "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 'gymnasium.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "low_repr": "-1.0", "high_repr": "1.0", "_np_random": null}, "n_envs": 4, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "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": "2.0.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.17742942217737437, "std_reward": 0.09965095997387749, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-11T21:26:26.076669"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:916cc2b632743fca170215c54448e79dfcb213199dd8b8c95071e3e0b12f42d5
+size 2623