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: -1.34 +/- 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 sha256:521bed53c3d00aefae22a78df661c76b778161456ba967ef134d983906ee7d71
+size 108108

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 0x7ff590d02160>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7ff590cf9c00>"
+    },
+    "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": 2000000,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1674029810652488458,
+    "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": "[[ 0.45175925 -0.01029871  0.5468636 ]\n [ 0.45175925 -0.01029871  0.5468636 ]\n [ 0.45175925 -0.01029871  0.5468636 ]\n [ 0.45175925 -0.01029871  0.5468636 ]]",
+        "desired_goal": "[[-0.03190119  1.2530029   1.1989834 ]\n [ 0.9341146   1.5313894  -0.5995817 ]\n [-0.7507732   1.6441209   0.13224618]\n [-0.9318747   1.4971172   0.9734516 ]]",
+        "observation": "[[ 4.5175925e-01 -1.0298714e-02  5.4686362e-01 -4.5324662e-03\n   2.0566421e-04 -4.3500341e-03]\n [ 4.5175925e-01 -1.0298714e-02  5.4686362e-01 -4.5324662e-03\n   2.0566421e-04 -4.3500341e-03]\n [ 4.5175925e-01 -1.0298714e-02  5.4686362e-01 -4.5324662e-03\n   2.0566421e-04 -4.3500341e-03]\n [ 4.5175925e-01 -1.0298714e-02  5.4686362e-01 -4.5324662e-03\n   2.0566421e-04 -4.3500341e-03]]"
+    },
+    "_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": "[[-0.06178907 -0.06732233  0.14129384]\n [ 0.10492398  0.05817115  0.18686232]\n [ 0.01546848  0.13646151  0.23090775]\n [-0.06017195  0.10282966  0.1925878 ]]",
+        "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": 100000,
+    "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:e68a7d4a67a9d7063effc0c4ddf3df43d1d7569d6aa634e728e74e9072744ece
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ea9df3bfffe633a2665ae0ec5979a775d1e775c852cb54796de8514578bb7124
+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-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.0+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- 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 0x7ff590d02160>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7ff590cf9c00>"}, "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": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1674029810652488458, "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": "[[ 0.45175925 -0.01029871  0.5468636 ]\n [ 0.45175925 -0.01029871  0.5468636 ]\n [ 0.45175925 -0.01029871  0.5468636 ]\n [ 0.45175925 -0.01029871  0.5468636 ]]", "desired_goal": "[[-0.03190119  1.2530029   1.1989834 ]\n [ 0.9341146   1.5313894  -0.5995817 ]\n [-0.7507732   1.6441209   0.13224618]\n [-0.9318747   1.4971172   0.9734516 ]]", "observation": "[[ 4.5175925e-01 -1.0298714e-02  5.4686362e-01 -4.5324662e-03\n   2.0566421e-04 -4.3500341e-03]\n [ 4.5175925e-01 -1.0298714e-02  5.4686362e-01 -4.5324662e-03\n   2.0566421e-04 -4.3500341e-03]\n [ 4.5175925e-01 -1.0298714e-02  5.4686362e-01 -4.5324662e-03\n   2.0566421e-04 -4.3500341e-03]\n [ 4.5175925e-01 -1.0298714e-02  5.4686362e-01 -4.5324662e-03\n   2.0566421e-04 -4.3500341e-03]]"}, "_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": "[[-0.06178907 -0.06732233  0.14129384]\n [ 0.10492398  0.05817115  0.18686232]\n [ 0.01546848  0.13646151  0.23090775]\n [-0.06017195  0.10282966  0.1925878 ]]", "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": 100000, "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-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -1.3353335775434971, "std_reward": 0.3020323513490869, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-18T10:05:03.112714"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aa2fa8aaf5fbd1d8ab9cd2faf3ef375565edf9396c77af07be93806d9f644a7a
+size 3212