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: -0.92 +/- 0.34
+      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 sha256:5fed43e455b7c802e9bd35c69af515c7504adf1a9306db62ba7ba4aca2ed04ca
+size 109555

a2c-PandaReachDense-v2/_stable_baselines3_version

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

a2c-PandaReachDense-v2/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 0x7fd780da3820>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fd780d9c8c0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu",
+        "log_std_init": -2,
+        "ortho_init": false,
+        "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": 1681339487692450291,
+    "learning_rate": 0.00096,
+    "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": "[[0.35085458 0.00061143 0.5661985 ]\n [0.35085458 0.00061143 0.5661985 ]\n [0.35085458 0.00061143 0.5661985 ]\n [0.35085458 0.00061143 0.5661985 ]]",
+        "desired_goal": "[[ 1.4568491   0.71789277  0.50441843]\n [-0.14196377  0.42326692 -1.4543905 ]\n [ 0.1613049  -0.44487095 -0.3752416 ]\n [ 1.65043     0.36593103 -0.17754288]]",
+        "observation": "[[ 0.35085458  0.00061143  0.5661985   0.07080963 -0.00171782  0.06534009]\n [ 0.35085458  0.00061143  0.5661985   0.07080963 -0.00171782  0.06534009]\n [ 0.35085458  0.00061143  0.5661985   0.07080963 -0.00171782  0.06534009]\n [ 0.35085458  0.00061143  0.5661985   0.07080963 -0.00171782  0.06534009]]"
+    },
+    "_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": "[[-0.08056005  0.13468118  0.00427332]\n [ 0.10332917 -0.09474913  0.19128025]\n [ 0.1012511  -0.06966022  0.0569705 ]\n [ 0.04217834  0.14788298  0.12360226]]",
+        "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": true,
+    "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": 31250,
+    "n_steps": 8,
+    "gamma": 0.99,
+    "gae_lambda": 0.9,
+    "ent_coef": 0.0,
+    "vf_coef": 0.4,
+    "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 sha256:07c10647eb340b7990b6badb2f8af8742073b38c33d73241c82e34a2dc2dd92e
+size 45438

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c6399cbce75a1d4dd73c9f3a6c66336017a7fbbc6e09e2ea31941b4633fb6db7
+size 46718

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: 1.8.0
+- PyTorch: 2.0.0+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 0x7fd780da3820>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fd780d9c8c0>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "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": 1681339487692450291, "learning_rate": 0.00096, "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": "[[0.35085458 0.00061143 0.5661985 ]\n [0.35085458 0.00061143 0.5661985 ]\n [0.35085458 0.00061143 0.5661985 ]\n [0.35085458 0.00061143 0.5661985 ]]", "desired_goal": "[[ 1.4568491   0.71789277  0.50441843]\n [-0.14196377  0.42326692 -1.4543905 ]\n [ 0.1613049  -0.44487095 -0.3752416 ]\n [ 1.65043     0.36593103 -0.17754288]]", "observation": "[[ 0.35085458  0.00061143  0.5661985   0.07080963 -0.00171782  0.06534009]\n [ 0.35085458  0.00061143  0.5661985   0.07080963 -0.00171782  0.06534009]\n [ 0.35085458  0.00061143  0.5661985   0.07080963 -0.00171782  0.06534009]\n [ 0.35085458  0.00061143  0.5661985   0.07080963 -0.00171782  0.06534009]]"}, "_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": "[[-0.08056005  0.13468118  0.00427332]\n [ 0.10332917 -0.09474913  0.19128025]\n [ 0.1012511  -0.06966022  0.0569705 ]\n [ 0.04217834  0.14788298  0.12360226]]", "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": true, "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": 31250, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "max_grad_norm": 0.5, "normalize_advantage": false, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "gAWVUgMAAAAAAACMD2d5bS5zcGFjZXMuZGljdJSMBERpY3SUk5QpgZR9lCiMBnNwYWNlc5SMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwOZ3ltLnNwYWNlcy5ib3iUjANCb3iUk5QpgZR9lCiMBWR0eXBllIwFbnVtcHmUaBCTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowGX3NoYXBllEsDhZSMA2xvd5SMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFUsDhZSMAUOUdJRSlIwEaGlnaJRoHSiWDAAAAAAAAAAAACBBAAAgQQAAIEGUaBVLA4WUaCB0lFKUjA1ib3VuZGVkX2JlbG93lGgdKJYDAAAAAAAAAAEBAZRoEowCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksDhZRoIHSUUpSMDWJvdW5kZWRfYWJvdmWUaB0olgMAAAAAAAAAAQEBlGgsSwOFlGggdJRSlIwKX25wX3JhbmRvbZROdWKMDGRlc2lyZWRfZ29hbJRoDSmBlH2UKGgQaBVoGEsDhZRoGmgdKJYMAAAAAAAAAAAAIMEAACDBAAAgwZRoFUsDhZRoIHSUUpRoI2gdKJYMAAAAAAAAAAAAIEEAACBBAAAgQZRoFUsDhZRoIHSUUpRoKGgdKJYDAAAAAAAAAAEBAZRoLEsDhZRoIHSUUpRoMmgdKJYDAAAAAAAAAAEBAZRoLEsDhZRoIHSUUpRoN051YowLb2JzZXJ2YXRpb26UaA0pgZR9lChoEGgVaBhLBoWUaBpoHSiWGAAAAAAAAAAAACDBAAAgwQAAIMEAACDBAAAgwQAAIMGUaBVLBoWUaCB0lFKUaCNoHSiWGAAAAAAAAAAAACBBAAAgQQAAIEEAACBBAAAgQQAAIEGUaBVLBoWUaCB0lFKUaChoHSiWBgAAAAAAAAABAQEBAQGUaCxLBoWUaCB0lFKUaDJoHSiWBgAAAAAAAAABAQEBAQGUaCxLBoWUaCB0lFKUaDdOdWJ1aBhOaBBOaDdOdWIu", "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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.9208339402917772, "std_reward": 0.3386715363472446, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-12T23:35:34.419886"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ae702066e892ac4712016b897544f5b1a7fc8eb8023aa039e8add500bf2a0fcb
+size 2381