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: -1.00 +/- 0.35
+      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 sha256:54c309aa25f73977cdebd634113f1df34834967132f3e179615d2c20737fd44e
+size 108141

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 0x7f7ae5787d90>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f7ae577ed00>"
+    },
+    "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:": "gAWVcwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLA4WUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaAtLA4WUjAFDlHSUUpSMBGhpZ2iUaBMolgwAAAAAAAAAAACAPwAAgD8AAIA/lGgLSwOFlGgWdJRSlIwNYm91bmRlZF9iZWxvd5RoEyiWAwAAAAAAAAABAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUaBZ0lFKUjA1ib3VuZGVkX2Fib3ZllGgTKJYDAAAAAAAAAAEBAZRoIksDhZRoFnSUUpSMCl9ucF9yYW5kb22UTnViLg==",
+        "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": 1678565900024951859,
+    "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": "[[0.4004341  0.01657987 0.58126456]\n [0.4004341  0.01657987 0.58126456]\n [0.4004341  0.01657987 0.58126456]\n [0.4004341  0.01657987 0.58126456]]",
+        "desired_goal": "[[-0.03107026  0.7550449   1.2399056 ]\n [-0.45590192  0.2334774   0.9658718 ]\n [ 1.1747588  -1.0518426  -0.9161243 ]\n [ 1.5577207   1.6607066   0.8860572 ]]",
+        "observation": "[[ 0.4004341   0.01657987  0.58126456 -0.01510654  0.00296441 -0.00879119]\n [ 0.4004341   0.01657987  0.58126456 -0.01510654  0.00296441 -0.00879119]\n [ 0.4004341   0.01657987  0.58126456 -0.01510654  0.00296441 -0.00879119]\n [ 0.4004341   0.01657987  0.58126456 -0.01510654  0.00296441 -0.00879119]]"
+    },
+    "_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": "[[ 0.08819134 -0.11224464  0.088728  ]\n [-0.01737622 -0.05956658  0.01118517]\n [-0.00319888  0.09048294  0.17599173]\n [-0.05824677  0.06532507  0.16190325]]",
+        "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:e6bd749212679feadebb681c0dcba1a5c6d7b9b5125245a0e6ba8a78feb30910
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9d5ee3232ebac2a1b2b421f35d370332a85bd16999ceba01d40b1327ca2355bc
+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-5.19.0-35-generic-x86_64-with-glibc2.35 # 36~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Fri Feb 17 15:17:25 UTC 2
+- Python: 3.10.6
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu117
+- GPU Enabled: True
+- Numpy: 1.23.3
+- 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 0x7f7ae5787d90>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f7ae577ed00>"}, "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": 1678565900024951859, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWV+QIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMYy9ob21lL2hhbmphY2svLnB5ZW52L3ZlcnNpb25zLzMuMTAuNi9saWIvcHl0aG9uMy4xMC9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgQBlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMYy9ob21lL2hhbmphY2svLnB5ZW52L3ZlcnNpb25zLzMuMTAuNi9saWIvcHl0aG9uMy4xMC9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/RvAGjbi6x4WUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[0.4004341  0.01657987 0.58126456]\n [0.4004341  0.01657987 0.58126456]\n [0.4004341  0.01657987 0.58126456]\n [0.4004341  0.01657987 0.58126456]]", "desired_goal": "[[-0.03107026  0.7550449   1.2399056 ]\n [-0.45590192  0.2334774   0.9658718 ]\n [ 1.1747588  -1.0518426  -0.9161243 ]\n [ 1.5577207   1.6607066   0.8860572 ]]", "observation": "[[ 0.4004341   0.01657987  0.58126456 -0.01510654  0.00296441 -0.00879119]\n [ 0.4004341   0.01657987  0.58126456 -0.01510654  0.00296441 -0.00879119]\n [ 0.4004341   0.01657987  0.58126456 -0.01510654  0.00296441 -0.00879119]\n [ 0.4004341   0.01657987  0.58126456 -0.01510654  0.00296441 -0.00879119]]"}, "_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": "[[ 0.08819134 -0.11224464  0.088728  ]\n [-0.01737622 -0.05956658  0.01118517]\n [-0.00319888  0.09048294  0.17599173]\n [-0.05824677  0.06532507  0.16190325]]", "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-5.19.0-35-generic-x86_64-with-glibc2.35 # 36~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Fri Feb 17 15:17:25 UTC 2", "Python": "3.10.6", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu117", "GPU Enabled": "True", "Numpy": "1.23.3", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.9974773038877174, "std_reward": 0.3532670624349992, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-12T05:33:55.246094"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:66e8d14b0428e8297f7598c1d760746078bc2350cf26e151bc3e63dcb2be3515
+size 3117