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: -0.67 +/- 0.16
+      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:eba250cbae2ac0c54faf1a4ff8a373f441adfed58c0d16d541e0548d1b2f5d06
+size 108043

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 0x7fb566784f70>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fb566786280>"
+    },
+    "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:": "gAWVbQEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLA4WUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaApLA4WUjAFDlHSUUpSMBGhpZ2iUaBIolgwAAAAAAAAAAACAPwAAgD8AAIA/lGgKSwOFlGgVdJRSlIwNYm91bmRlZF9iZWxvd5RoEiiWAwAAAAAAAAABAQGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYDAAAAAAAAAAEBAZRoIUsDhZRoFXSUUpSMCl9ucF9yYW5kb22UTnViLg==",
+        "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": 657344,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1678769546077838517,
+    "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": "[[ 1.6559911  -1.1655482   0.54909456]\n [-0.80668354 -0.04999414 -0.7659625 ]\n [ 1.2512227  -1.3586924  -0.57110673]\n [ 1.0878302   1.5464871  -0.91462004]]",
+        "desired_goal": "[[ 1.6924703  -1.1372452   0.5618115 ]\n [-0.826028   -0.11926074 -0.72120005]\n [ 1.2867401  -1.3364667  -0.5473763 ]\n [ 1.0881001   1.4041427  -0.9781845 ]]",
+        "observation": "[[ 1.6559911  -1.1655482   0.54909456  0.02833421 -0.05941187  0.02204823]\n [-0.80668354 -0.04999414 -0.7659625  -0.01274296 -0.01424844 -0.26799923]\n [ 1.2512227  -1.3586924  -0.57110673  0.05983594 -0.03595509 -0.0326787 ]\n [ 1.0878302   1.5464871  -0.91462004  0.03954871  0.05220764 -0.00317354]]"
+    },
+    "_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": "[[ 0.13267975 -0.10021639  0.21389145]\n [ 0.09208829 -0.0861964   0.05809796]\n [ 0.06975584 -0.08478618  0.11146554]\n [ 0.07993101 -0.07091036  0.12089408]]",
+        "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": 0.34265999999999996,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 32867,
+    "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 sha256:4ca92f30d0ef0c6fbd1e11e25df382b404193187a382914313ce995230a3c687
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1a8851e295405af2e6b27695a31db34ad05d0bc86f1e27d55ca87c4d3a70618e
+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 0x7fb566784f70>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fb566786280>"}, "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": 657344, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1678769546077838517, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAhPfTP68wlb92kQw/0IJOv6jGTL0eFkS/ESigP6Lprb8NNBK/BT6LP0rzxT+KJGq/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAA3qLYP0CRkb/h0g8/knZTv/k+9L2RoDi/5rOkP1cRq7/aIAy/3UaLP/O6sz9Nanq/lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAACE99M/rzCVv3aRDD8nHeg83FlzvX2etDzQgk6/qMZMvR4WRL/bx1C8TXJpvDI3ib4RKKA/oumtvw00Er+IFnU9pkUTvRvaBb0FPos/SvPFP4okar/U/SE9rtdVPSD7T7uUaA5LBEsGhpRoEnSUUpR1Lg==", "achieved_goal": "[[ 1.6559911  -1.1655482   0.54909456]\n [-0.80668354 -0.04999414 -0.7659625 ]\n [ 1.2512227  -1.3586924  -0.57110673]\n [ 1.0878302   1.5464871  -0.91462004]]", "desired_goal": "[[ 1.6924703  -1.1372452   0.5618115 ]\n [-0.826028   -0.11926074 -0.72120005]\n [ 1.2867401  -1.3364667  -0.5473763 ]\n [ 1.0881001   1.4041427  -0.9781845 ]]", "observation": "[[ 1.6559911  -1.1655482   0.54909456  0.02833421 -0.05941187  0.02204823]\n [-0.80668354 -0.04999414 -0.7659625  -0.01274296 -0.01424844 -0.26799923]\n [ 1.2512227  -1.3586924  -0.57110673  0.05983594 -0.03595509 -0.0326787 ]\n [ 1.0878302   1.5464871  -0.91462004  0.03954871  0.05220764 -0.00317354]]"}, "_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": "[[ 0.13267975 -0.10021639  0.21389145]\n [ 0.09208829 -0.0861964   0.05809796]\n [ 0.06975584 -0.08478618  0.11146554]\n [ 0.07993101 -0.07091036  0.12089408]]", "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": 0.34265999999999996, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 32867, "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-glibc2.31 # 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": -0.6724932713783346, "std_reward": 0.1633396543075007, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-14T05:31:42.324395"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ff9b0b52a3a9770f8644fb30ccd89f819ed07a8e11df1844513bd44349ed5b95
+size 3056