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: -3.27 +/- 0.81
+      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 sha256:697d52f8e7fdcbf2577b3cdfd763fe003ed8af2486d38fd4154a04748841d7ab
+size 108075

a2c-PandaReachDense-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

a2c-PandaReachDense-v2/data

@@ -0,0 +1,95 @@
+{
+    "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 0x7fc7061736d0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fc70616eb00>"
+    },
+    "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": 1685476686243184822,
+    "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": "[[ 0.47092262 -0.04561051  0.63191324]\n [ 0.47092262 -0.04561051  0.63191324]\n [ 0.47092262 -0.04561051  0.63191324]\n [ 0.47092262 -0.04561051  0.63191324]]",
+        "desired_goal": "[[ 0.72929376  0.64779943 -0.55094695]\n [ 0.91224307  1.574038    0.12198813]\n [-0.648636   -1.3435715  -0.4591695 ]\n [-1.6121179   0.95603526 -0.17961223]]",
+        "observation": "[[ 0.47092262 -0.04561051  0.63191324  0.01047682  0.00225498  0.01804622]\n [ 0.47092262 -0.04561051  0.63191324  0.01047682  0.00225498  0.01804622]\n [ 0.47092262 -0.04561051  0.63191324  0.01047682  0.00225498  0.01804622]\n [ 0.47092262 -0.04561051  0.63191324  0.01047682  0.00225498  0.01804622]]"
+    },
+    "_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": "[[-0.0943054   0.04069844  0.08816651]\n [-0.03215456 -0.05227187  0.07439102]\n [-0.08161719  0.02013827  0.23075505]\n [-0.07508568  0.06619418  0.09635854]]",
+        "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 '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
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b4794b2398666a336e1385fd775559c5627b801c4faeeb63db19f8e9f315b0a6
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a80da8bfd846b5bd1ae9489478aff6970aae5b13719e3b519fa880c630a95f8d
+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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
+- Python: 3.10.11
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.1+cu118
+- 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 0x7fc7061736d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fc70616eb00>"}, "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": 1685476686243184822, "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": "[[ 0.47092262 -0.04561051  0.63191324]\n [ 0.47092262 -0.04561051  0.63191324]\n [ 0.47092262 -0.04561051  0.63191324]\n [ 0.47092262 -0.04561051  0.63191324]]", "desired_goal": "[[ 0.72929376  0.64779943 -0.55094695]\n [ 0.91224307  1.574038    0.12198813]\n [-0.648636   -1.3435715  -0.4591695 ]\n [-1.6121179   0.95603526 -0.17961223]]", "observation": "[[ 0.47092262 -0.04561051  0.63191324  0.01047682  0.00225498  0.01804622]\n [ 0.47092262 -0.04561051  0.63191324  0.01047682  0.00225498  0.01804622]\n [ 0.47092262 -0.04561051  0.63191324  0.01047682  0.00225498  0.01804622]\n [ 0.47092262 -0.04561051  0.63191324  0.01047682  0.00225498  0.01804622]]"}, "_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": "[[-0.0943054   0.04069844  0.08816651]\n [-0.03215456 -0.05227187  0.07439102]\n [-0.08161719  0.02013827  0.23075505]\n [-0.07508568  0.06619418  0.09635854]]", "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 '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, "system_info": {"OS": "Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.11", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -3.2746466415934266, "std_reward": 0.8103908855437006, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-30T21:09:41.356973"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:296d48aba6e44cecc992ac20c9bcc062645bb7c0c09cbaa54bf843c608f59de0
+size 2387