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: -0.22 +/- 0.11
+      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 sha256:04c6893edcb15886b93bed59aae08522a3ef94b20213d592078ef83b3f5e65ee
+size 106831

a2c-PandaReachDense-v3/_stable_baselines3_version

@@ -0,0 +1 @@
+2.1.0

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 0x7acb427a77f0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7acb4279af00>"
+    },
+    "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": 1692357881238875533,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[-0.06947608 -0.43897966 -0.24979083]\n [-0.64274323 -0.23327895  0.30034867]\n [ 0.24822974  0.00700017  0.41175812]\n [-0.11863662  0.43638098 -0.2269474 ]]",
+        "desired_goal": "[[ 0.63378185 -0.93782216 -0.67464095]\n [-1.1042842  -0.01404054  0.345062  ]\n [ 0.22313406 -1.2657622  -0.7773132 ]\n [-0.60014457  1.0029275  -1.4649955 ]]",
+        "observation": "[[-0.06947608 -0.43897966 -0.24979083 -1.6948644  -1.7059069  -1.2841167 ]\n [-0.64274323 -0.23327895  0.30034867 -0.91262615 -0.24630986  0.84286666]\n [ 0.24822974  0.00700017  0.41175812  0.4803179  -0.0023435   0.38719863]\n [-0.11863662  0.43638098 -0.2269474  -1.8865192   1.6718695  -1.3851899 ]]"
+    },
+    "_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": "[[-0.09725976  0.09184135  0.21331377]\n [ 0.06788258  0.08909526  0.04246507]\n [ 0.04033819 -0.09555022  0.07010698]\n [ 0.10853254  0.10713658  0.06149088]]",
+        "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 sha256:156d18ed964f5b0ed72317abc12821838d815a181e6c33f00d4ff61956109776
+size 44734

a2c-PandaReachDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d7203eb52d9c51dff2aea53db541746fe0688f9ae871ab29dcf5f3b261c28a06
+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: 2.1.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.29.0
+- 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 0x7acb427a77f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7acb4279af00>"}, "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": 1692357881238875533, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[-0.06947608 -0.43897966 -0.24979083]\n [-0.64274323 -0.23327895  0.30034867]\n [ 0.24822974  0.00700017  0.41175812]\n [-0.11863662  0.43638098 -0.2269474 ]]", "desired_goal": "[[ 0.63378185 -0.93782216 -0.67464095]\n [-1.1042842  -0.01404054  0.345062  ]\n [ 0.22313406 -1.2657622  -0.7773132 ]\n [-0.60014457  1.0029275  -1.4649955 ]]", "observation": "[[-0.06947608 -0.43897966 -0.24979083 -1.6948644  -1.7059069  -1.2841167 ]\n [-0.64274323 -0.23327895  0.30034867 -0.91262615 -0.24630986  0.84286666]\n [ 0.24822974  0.00700017  0.41175812  0.4803179  -0.0023435   0.38719863]\n [-0.11863662  0.43638098 -0.2269474  -1.8865192   1.6718695  -1.3851899 ]]"}, "_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": "[[-0.09725976  0.09184135  0.21331377]\n [ 0.06788258  0.08909526  0.04246507]\n [ 0.04033819 -0.09555022  0.07010698]\n [ 0.10853254  0.10713658  0.06149088]]", "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:": "gAWVnQEAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAwAAAAAAAAABAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUjAFDlHSUUpSMDWJvdW5kZWRfYWJvdmWUaBEolgMAAAAAAAAAAQEBlGgVSwOFlGgZdJRSlIwGX3NoYXBllEsDhZSMA2xvd5RoESiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaAtLA4WUaBl0lFKUjARoaWdolGgRKJYMAAAAAAAAAAAAgD8AAIA/AACAP5RoC0sDhZRoGXSUUpSMCGxvd19yZXBylIwELTEuMJSMCWhpZ2hfcmVwcpSMAzEuMJSMCl9ucF9yYW5kb22UTnViLg==", "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": "2.1.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.29.0", "OpenAI Gym": "0.25.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.2203746870160103, "std_reward": 0.11039451782879083, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-18T12:13:02.179346"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3d11642f11e96a9d8c929dab156e5e626408a9331a74585912da1e3aecd87e95
+size 2623