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: -1.15 +/- 0.11
+      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 sha256:9b6db36d845edc4079d45c683edd8867a099d3b42fc2deb4a7bcbc8f46e3f7d9
+size 108031

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 0x7f84a2d46ee0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f84a2d49780>"
+    },
+    "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": 282112,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1678701563809540483,
+    "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": "[[-1.5403537   0.7552569   0.7044475 ]\n [-1.1956452   1.5570772  -0.6214484 ]\n [ 0.6854068   0.23648559 -0.9530282 ]\n [ 0.6175385  -0.8315029   0.9113213 ]]",
+        "desired_goal": "[[-1.1260961   0.9116774   1.1880647 ]\n [-1.2159473   1.5978199  -0.69264203]\n [ 0.36287874  0.31416017 -1.35276   ]\n [ 0.542116   -0.8266104   0.881964  ]]",
+        "observation": "[[-1.5403537   0.7552569   0.7044475   0.9356513  -1.9522207   0.2961697 ]\n [-1.1956452   1.5570772  -0.6214484  -0.04430206 -1.1473783   0.08982814]\n [ 0.6854068   0.23648559 -0.9530282  -0.13135277  1.705981   -0.8853156 ]\n [ 0.6175385  -0.8315029   0.9113213   0.34396034  1.7048358   1.1984941 ]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[-0.00103104  0.14756715  0.07623578]\n [ 0.11453198  0.08930361  0.18563859]\n [ 0.10234462  0.05763686  0.22031282]\n [ 0.03355822  0.09384026  0.07631244]]",
+        "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": 0.71792,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 7052,
+    "n_steps": 10,
+    "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:db2466538d6e162e4fa2184ddbb04eae1bd7482c9d7bda8330839e339f63ed29
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:93dde78ce525e7e7a5949f9fcb25768362d83e24ec7aa5fdbed4f7d4dbc6a62b
+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: 1.13.1+cu116
+- 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 0x7f84a2d46ee0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f84a2d49780>"}, "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": 282112, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1678701563809540483, "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": "[[-1.5403537   0.7552569   0.7044475 ]\n [-1.1956452   1.5570772  -0.6214484 ]\n [ 0.6854068   0.23648559 -0.9530282 ]\n [ 0.6175385  -0.8315029   0.9113213 ]]", "desired_goal": "[[-1.1260961   0.9116774   1.1880647 ]\n [-1.2159473   1.5978199  -0.69264203]\n [ 0.36287874  0.31416017 -1.35276   ]\n [ 0.542116   -0.8266104   0.881964  ]]", "observation": "[[-1.5403537   0.7552569   0.7044475   0.9356513  -1.9522207   0.2961697 ]\n [-1.1956452   1.5570772  -0.6214484  -0.04430206 -1.1473783   0.08982814]\n [ 0.6854068   0.23648559 -0.9530282  -0.13135277  1.705981   -0.8853156 ]\n [ 0.6175385  -0.8315029   0.9113213   0.34396034  1.7048358   1.1984941 ]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[-0.00103104  0.14756715  0.07623578]\n [ 0.11453198  0.08930361  0.18563859]\n [ 0.10234462  0.05763686  0.22031282]\n [ 0.03355822  0.09384026  0.07631244]]", "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": 0.71792, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 7052, "n_steps": 10, "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": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -1.1509346106322482, "std_reward": 0.10503895013031116, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-13T10:13:50.589738"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3cfa69f1d1361fb82c7bb3d91c540ca40ab2ec8f0ca73374fd1e8e4fd22a40a6
+size 3056