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: -3.33 +/- 0.99
+      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 sha256:6314e6550aa65e8e619af2e043c347b2757e043389e20fce871f5b485838f9d3
+size 108011

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 0x7f5d7983f4c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f5d798b8a80>"
+    },
+    "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:": "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,
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1677330254418491139,
+    "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": "[[0.43764603 0.0304075  0.57747257]\n [0.43764603 0.0304075  0.57747257]\n [0.43764603 0.0304075  0.57747257]\n [0.43764603 0.0304075  0.57747257]]",
+        "desired_goal": "[[-1.4323006  -1.4532651   1.2914959 ]\n [-1.4230847   1.2450033   1.0998486 ]\n [-0.32899752 -0.1101225   1.2666028 ]\n [-0.5354858   1.5757334   0.3578254 ]]",
+        "observation": "[[ 0.43764603  0.0304075   0.57747257  0.009774    0.00420713 -0.0036161 ]\n [ 0.43764603  0.0304075   0.57747257  0.009774    0.00420713 -0.0036161 ]\n [ 0.43764603  0.0304075   0.57747257  0.009774    0.00420713 -0.0036161 ]\n [ 0.43764603  0.0304075   0.57747257  0.009774    0.00420713 -0.0036161 ]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAA6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+6nIdPRlsGqxDI0o+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAA3wSQPdZi2D3FZW4+n1ULPoOF/j3zWPI9wdcQvrkhuLyxn189musBPjqDtL0UEmw+lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAACUaA5LBEsGhpRoEnSUUpR1Lg==",
+        "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": "[[ 0.07032179  0.10565726  0.2328101 ]\n [ 0.1360688   0.12427809  0.11833372]\n [-0.14144804 -0.02247702  0.05459565]\n [ 0.12687531 -0.08814092  0.23053771]]",
+        "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 sha256:611947cce11385882bc41f633c82da73a1ebb25fc898c7ca7c6ebb32424993c0
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:97647447929bd96bb930a71b8ab0c1a9323a62eb282f33c557917859c69bd163
+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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- 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 0x7f5d7983f4c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f5d798b8a80>"}, "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": 1677330254418491139, "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": "[[0.43764603 0.0304075  0.57747257]\n [0.43764603 0.0304075  0.57747257]\n [0.43764603 0.0304075  0.57747257]\n [0.43764603 0.0304075  0.57747257]]", "desired_goal": "[[-1.4323006  -1.4532651   1.2914959 ]\n [-1.4230847   1.2450033   1.0998486 ]\n [-0.32899752 -0.1101225   1.2666028 ]\n [-0.5354858   1.5757334   0.3578254 ]]", "observation": "[[ 0.43764603  0.0304075   0.57747257  0.009774    0.00420713 -0.0036161 ]\n [ 0.43764603  0.0304075   0.57747257  0.009774    0.00420713 -0.0036161 ]\n [ 0.43764603  0.0304075   0.57747257  0.009774    0.00420713 -0.0036161 ]\n [ 0.43764603  0.0304075   0.57747257  0.009774    0.00420713 -0.0036161 ]]"}, "_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": "[[ 0.07032179  0.10565726  0.2328101 ]\n [ 0.1360688   0.12427809  0.11833372]\n [-0.14144804 -0.02247702  0.05459565]\n [ 0.12687531 -0.08814092  0.23053771]]", "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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "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": -3.328794828010723, "std_reward": 0.9857517267179162, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-25T13:53:36.170737"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:07d5d3784d3523927fa35f7489806381ff49bd320f39ef710ab67f7f7bee1d1c
+size 3056