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: -4.65 +/- 1.48
+      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:b4d56a98c4b2055a3d25dd7b2382ae8d1a86da49fea9c0287daf1c27e1675c9c
+size 108023

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 0x7feb44c1c040>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7feb44c155a0>"
+    },
+    "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": 1676481811148035793,
+    "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": "[[ 0.4685696  -0.0511881   0.60816914]\n [ 0.4685696  -0.0511881   0.60816914]\n [ 0.4685696  -0.0511881   0.60816914]\n [ 0.4685696  -0.0511881   0.60816914]]",
+        "desired_goal": "[[-1.167246    0.19584097  0.10897754]\n [-1.5806127   0.36783516  0.7564356 ]\n [ 0.76836014 -0.81954736 -0.23970188]\n [ 0.9146735   0.34976014  0.8156834 ]]",
+        "observation": "[[ 0.4685696  -0.0511881   0.60816914  0.01053369 -0.00304974  0.01442839]\n [ 0.4685696  -0.0511881   0.60816914  0.01053369 -0.00304974  0.01442839]\n [ 0.4685696  -0.0511881   0.60816914  0.01053369 -0.00304974  0.01442839]\n [ 0.4685696  -0.0511881   0.60816914  0.01053369 -0.00304974  0.01442839]]"
+    },
+    "_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": "[[-0.10632192 -0.11312019  0.03641324]\n [ 0.06568749 -0.06037723  0.07961464]\n [-0.0751064  -0.06161527  0.24935997]\n [-0.13669913 -0.04753882  0.23464084]]",
+        "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:aada1c84a5c269375eb6b8d0e7f29bf030e3bf7596a402b9cfa49a5aec9a9b0b
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a201882481bf8ea973c972d1d73c7eca3ad559b3d7c112075204ef55f5a42413
+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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+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 0x7feb44c1c040>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7feb44c155a0>"}, "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": 1676481811148035793, "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": "[[ 0.4685696  -0.0511881   0.60816914]\n [ 0.4685696  -0.0511881   0.60816914]\n [ 0.4685696  -0.0511881   0.60816914]\n [ 0.4685696  -0.0511881   0.60816914]]", "desired_goal": "[[-1.167246    0.19584097  0.10897754]\n [-1.5806127   0.36783516  0.7564356 ]\n [ 0.76836014 -0.81954736 -0.23970188]\n [ 0.9146735   0.34976014  0.8156834 ]]", "observation": "[[ 0.4685696  -0.0511881   0.60816914  0.01053369 -0.00304974  0.01442839]\n [ 0.4685696  -0.0511881   0.60816914  0.01053369 -0.00304974  0.01442839]\n [ 0.4685696  -0.0511881   0.60816914  0.01053369 -0.00304974  0.01442839]\n [ 0.4685696  -0.0511881   0.60816914  0.01053369 -0.00304974  0.01442839]]"}, "_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": "[[-0.10632192 -0.11312019  0.03641324]\n [ 0.06568749 -0.06037723  0.07961464]\n [-0.0751064  -0.06161527  0.24935997]\n [-0.13669913 -0.04753882  0.23464084]]", "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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -4.648786566127091, "std_reward": 1.4750925046964616, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-15T18:18:00.106120"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a4f6b424ce1dc45e66e7e17f2aeebec8e03de48262a00588c71570848c98da6
+size 3056