Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v3
+      type: PandaReachDense-v3
+    metrics:
+    - type: mean_reward
+      value: -0.19 +/- 0.12
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaReachDense-v3**
+This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
+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-v3.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6815aed10ac010dc263b20bfe1ea6a7168884687e2827a50688b33998bf5e0fe
+size 106834

a2c-PandaReachDense-v3/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0

a2c-PandaReachDense-v3/data

@@ -0,0 +1,97 @@
+{
+    "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 0x7eb6933b57e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7eb6933b8500>"
+    },
+    "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": 743460,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1691564765937308292,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAW6GCP2A6wL5sO3I+5MRsPuh1QLudcss+78Rcvieslz9NSoa/jVsbv6rFxz4vSZs+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAWPC+P8WMjr/A8xq/2OTSvisSmT+qu0m/hkkMP2dqvz8hkAS+sUlXvxpEWT8Ll9k/lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAABboYI/YDrAvmw7cj7dgco/M3PIv2Eqkb/kxGw+6HVAu51yyz5Zyu8+eMk/u96dwD7vxFy+J6yXP01Khr+Eoq0/63ReP9HsOr+NWxu/qsXHPi9Jmz5Nbke/P8HSP0z5XT+UaA5LBEsGhpRoEnSUUpR1Lg==",
+        "achieved_goal": "[[ 1.0205492  -0.37544537  0.2365548 ]\n [ 0.23121983 -0.00293672  0.3973588 ]\n [-0.21559499  1.1849412  -1.0491425 ]\n [-0.6068657   0.39017993  0.30329272]]",
+        "desired_goal": "[[ 1.4917097  -1.113671   -0.60528183]\n [-0.4119022   1.195867   -0.7880198 ]\n [ 0.5479969   1.4954346  -0.12945606]\n [-0.8409682   0.8486954   1.699922  ]]",
+        "observation": "[[ 1.0205492  -0.37544537  0.2365548   1.5820881  -1.5660156  -1.1341058 ]\n [ 0.23121983 -0.00293672  0.3973588   0.46834067 -0.00292644  0.37620443]\n [-0.21559499  1.1849412  -1.0491425   1.3565221   0.8689715  -0.73017603]\n [-0.6068657   0.39017993  0.30329272 -0.7790268   1.6465224   0.8670852 ]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAABAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.05958456  0.03605155  0.02584123]\n [ 0.02006766 -0.05400949  0.21962336]\n [ 0.06738665 -0.06572045  0.22960454]\n [ 0.12159592  0.05364686  0.03023117]]",
+        "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": 0.25654,
+    "_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": 37173,
+    "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 'gymnasium.spaces.dict.Dict'>",
+        ":serialized:": "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",
+        "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])",
+        "_shape": null,
+        "dtype": null,
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "gAWVnQEAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAwAAAAAAAAABAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUjAFDlHSUUpSMDWJvdW5kZWRfYWJvdmWUaBEolgMAAAAAAAAAAQEBlGgVSwOFlGgZdJRSlIwGX3NoYXBllEsDhZSMA2xvd5RoESiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaAtLA4WUaBl0lFKUjARoaWdolGgRKJYMAAAAAAAAAAAAgD8AAIA/AACAP5RoC0sDhZRoGXSUUpSMCGxvd19yZXBylIwELTEuMJSMCWhpZ2hfcmVwcpSMAzEuMJSMCl9ucF9yYW5kb22UTnViLg==",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True]",
+        "bounded_above": "[ True  True  True]",
+        "_shape": [
+            3
+        ],
+        "low": "[-1. -1. -1.]",
+        "high": "[1. 1. 1.]",
+        "low_repr": "-1.0",
+        "high_repr": "1.0",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

a2c-PandaReachDense-v3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ad805ffc34ffd3564d15ff7e7c3a1daffda7d0a33b3c76bc9562d75a93851c63
+size 44734

a2c-PandaReachDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aa85328bfa898eac2ee0474c385adcfe46fba24984fe89ab16d1ad5bfbd12527
+size 46014

a2c-PandaReachDense-v3/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

a2c-PandaReachDense-v3/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

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 0x7eb6933b57e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7eb6933b8500>"}, "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": 743460, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1691564765937308292, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 1.0205492  -0.37544537  0.2365548 ]\n [ 0.23121983 -0.00293672  0.3973588 ]\n [-0.21559499  1.1849412  -1.0491425 ]\n [-0.6068657   0.39017993  0.30329272]]", "desired_goal": "[[ 1.4917097  -1.113671   -0.60528183]\n [-0.4119022   1.195867   -0.7880198 ]\n [ 0.5479969   1.4954346  -0.12945606]\n [-0.8409682   0.8486954   1.699922  ]]", "observation": "[[ 1.0205492  -0.37544537  0.2365548   1.5820881  -1.5660156  -1.1341058 ]\n [ 0.23121983 -0.00293672  0.3973588   0.46834067 -0.00292644  0.37620443]\n [-0.21559499  1.1849412  -1.0491425   1.3565221   0.8689715  -0.73017603]\n [-0.6068657   0.39017993  0.30329272 -0.7790268   1.6465224   0.8670852 ]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAABAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": "[[ 0.05958456  0.03605155  0.02584123]\n [ 0.02006766 -0.05400949  0.21962336]\n [ 0.06738665 -0.06572045  0.22960454]\n [ 0.12159592  0.05364686  0.03023117]]", "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": 0.25654, "_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": 37173, "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 'gymnasium.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box(-10.0, 10.0, (3,), float32)), ('desired_goal', Box(-10.0, 10.0, (3,), float32)), ('observation', Box(-10.0, 10.0, (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "low_repr": "-1.0", "high_repr": "1.0", "_np_random": null}, "n_envs": 4, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.19279725439846515, "std_reward": 0.11824635890072288, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-09T07:39:45.978431"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b8b85e3e2bf2c3c7b21613baf86265c3e2e129a875b59db87c125f77d0526196
+size 2623