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: -3.03 +/- 0.67
+      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 sha256:715b3421bb34fa4d1ca5cbad31396c90f01787ee934a844fab2672f698c1b6e2
+size 108159

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 0x7f8a47a51630>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f8a47a4ff40>"
+    },
+    "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": 1682845805258644565,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuCQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz9G8AaNuLrHhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 0.3610361  -0.01282438  0.5390543 ]\n [ 0.3610361  -0.01282438  0.5390543 ]\n [ 0.3610361  -0.01282438  0.5390543 ]\n [ 0.3610361  -0.01282438  0.5390543 ]]",
+        "desired_goal": "[[-0.68150455  1.2694758  -0.22101536]\n [ 0.15440048 -0.02600144  0.4406208 ]\n [ 1.6980622  -1.3960557  -0.0628187 ]\n [-0.5523274   1.6124201   1.3694493 ]]",
+        "observation": "[[ 3.6103609e-01 -1.2824376e-02  5.3905427e-01 -1.9426618e-03\n   2.7150835e-03 -2.2314377e-04]\n [ 3.6103609e-01 -1.2824376e-02  5.3905427e-01 -1.9426618e-03\n   2.7150835e-03 -2.2314377e-04]\n [ 3.6103609e-01 -1.2824376e-02  5.3905427e-01 -1.9426618e-03\n   2.7150835e-03 -2.2314377e-04]\n [ 3.6103609e-01 -1.2824376e-02  5.3905427e-01 -1.9426618e-03\n   2.7150835e-03 -2.2314377e-04]]"
+    },
+    "_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": "[[ 0.03963884 -0.13163558  0.28446978]\n [ 0.04070701 -0.10928604  0.22721724]\n [-0.09385555 -0.10378648  0.21171749]\n [ 0.08346745  0.02650699  0.16763003]]",
+        "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:b472366783a39c573c6beea461adf4f666502225693030bb5b64e7c646cc48f2
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8ef58cf467b68054e7e16fc97a9106f9f199c2bf24273ead073bf64d6137d245
+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-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.10.11
+- 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 0x7f8a47a51630>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f8a47a4ff40>"}, "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": 1682845805258644565, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAudm4PlUdUrx2/wk/udm4PlUdUrx2/wk/udm4PlUdUrx2/wk/udm4PlUdUrx2/wk/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAFXcuvy9+oj/aUWK+KRsePvgA1bwNmeE+GlrZP/Sxsr8Xp4C9VGUNv8hjzj8dSq8/lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAAC52bg+VR1SvHb/CT/qoP66i+8xO7P7abm52bg+VR1SvHb/CT/qoP66i+8xO7P7abm52bg+VR1SvHb/CT/qoP66i+8xO7P7abm52bg+VR1SvHb/CT/qoP66i+8xO7P7abmUaA5LBEsGhpRoEnSUUpR1Lg==", "achieved_goal": "[[ 0.3610361  -0.01282438  0.5390543 ]\n [ 0.3610361  -0.01282438  0.5390543 ]\n [ 0.3610361  -0.01282438  0.5390543 ]\n [ 0.3610361  -0.01282438  0.5390543 ]]", "desired_goal": "[[-0.68150455  1.2694758  -0.22101536]\n [ 0.15440048 -0.02600144  0.4406208 ]\n [ 1.6980622  -1.3960557  -0.0628187 ]\n [-0.5523274   1.6124201   1.3694493 ]]", "observation": "[[ 3.6103609e-01 -1.2824376e-02  5.3905427e-01 -1.9426618e-03\n   2.7150835e-03 -2.2314377e-04]\n [ 3.6103609e-01 -1.2824376e-02  5.3905427e-01 -1.9426618e-03\n   2.7150835e-03 -2.2314377e-04]\n [ 3.6103609e-01 -1.2824376e-02  5.3905427e-01 -1.9426618e-03\n   2.7150835e-03 -2.2314377e-04]\n [ 3.6103609e-01 -1.2824376e-02  5.3905427e-01 -1.9426618e-03\n   2.7150835e-03 -2.2314377e-04]]"}, "_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": "[[ 0.03963884 -0.13163558  0.28446978]\n [ 0.04070701 -0.10928604  0.22721724]\n [-0.09385555 -0.10378648  0.21171749]\n [ 0.08346745  0.02650699  0.16763003]]", "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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.10.11", "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": -3.032761904411018, "std_reward": 0.6731632521160817, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-30T10:01:08.387655"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:14e493e9a5ac29ff6866d47a50960c76f9c2d381221e37a135ce42c4dc8d0ac0
+size 2387