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: -0.38 +/- 0.17
+      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:f1644b6c0d62f1c3685a4ad154f03fc4d48289b5b4df841bd8e99556931ba81c
+size 109835

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 0x7f7fca316700>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f7fca313570>"
+    },
+    "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": 8,
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1676739829098334037,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/RvAGjbi6x4WUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "gAWVewIAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QolmAAAAAAAAAAg6vVPlCsATx9pxQ/g6vVPlCsATx9pxQ/g6vVPlCsATx9pxQ/g6vVPlCsATx9pxQ/g6vVPlCsATx9pxQ/g6vVPlCsATx9pxQ/g6vVPlCsATx9pxQ/g6vVPlCsATx9pxQ/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksISwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcolmAAAAAAAAAAnXIovzkAoz+/L9U/NZ/fPi6LvD+N8y0+Olkvvy0Ug7/L0Lk+ZIttP+4szz9IhAS+tKVQvpJriT9MEMC/s28Vv4cthj9UuZe/Kue0v37G0z/tMDa/LC/PPjVKlr5wlIe/lGgOSwhLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWwAAAAAAAAACDq9U+UKwBPH2nFD+J+Wo77ODKOrv1YTuDq9U+UKwBPH2nFD+J+Wo77ODKOrv1YTuDq9U+UKwBPH2nFD+J+Wo77ODKOrv1YTuDq9U+UKwBPH2nFD+J+Wo77ODKOrv1YTuDq9U+UKwBPH2nFD+J+Wo77ODKOrv1YTuDq9U+UKwBPH2nFD+J+Wo77ODKOrv1YTuDq9U+UKwBPH2nFD+J+Wo77ODKOrv1YTuDq9U+UKwBPH2nFD+J+Wo77ODKOrv1YTuUaA5LCEsGhpRoEnSUUpR1Lg==",
+        "achieved_goal": "[[0.41732416 0.00791462 0.58068067]\n [0.41732416 0.00791462 0.58068067]\n [0.41732416 0.00791462 0.58068067]\n [0.41732416 0.00791462 0.58068067]\n [0.41732416 0.00791462 0.58068067]\n [0.41732416 0.00791462 0.58068067]\n [0.41732416 0.00791462 0.58068067]\n [0.41732416 0.00791462 0.58068067]]",
+        "desired_goal": "[[-0.65799886  1.2734443   1.6655196 ]\n [ 0.43676153  1.4729974   0.16987438]\n [-0.68495524 -1.0240532   0.3629211 ]\n [ 0.9279082   1.6185586  -0.12941086]\n [-0.2037571   1.0735953  -1.5004973 ]\n [-0.58373564  1.0482644  -1.1853433 ]\n [-1.4133046   1.654495   -0.71168405]\n [ 0.40465677 -0.2935349  -1.0592175 ]]",
+        "observation": "[[0.41732416 0.00791462 0.58068067 0.00358543 0.00154784 0.00344787]\n [0.41732416 0.00791462 0.58068067 0.00358543 0.00154784 0.00344787]\n [0.41732416 0.00791462 0.58068067 0.00358543 0.00154784 0.00344787]\n [0.41732416 0.00791462 0.58068067 0.00358543 0.00154784 0.00344787]\n [0.41732416 0.00791462 0.58068067 0.00358543 0.00154784 0.00344787]\n [0.41732416 0.00791462 0.58068067 0.00358543 0.00154784 0.00344787]\n [0.41732416 0.00791462 0.58068067 0.00358543 0.00154784 0.00344787]\n [0.41732416 0.00791462 0.58068067 0.00358543 0.00154784 0.00344787]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVewAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYIAAAAAAAAAAEBAQEBAQEBlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlC4="
+    },
+    "_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]\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]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]",
+        "desired_goal": "[[ 0.10893544  0.01306038  0.22715451]\n [ 0.02876914  0.08919743  0.06549251]\n [ 0.06211166 -0.0143438   0.15299623]\n [ 0.00295165  0.04722655  0.17781171]\n [ 0.04971906  0.04285478  0.06695668]\n [ 0.02347497  0.04196865  0.279613  ]\n [-0.04294747 -0.0660387   0.20341207]\n [-0.11268704  0.00961813  0.2935208 ]]",
+        "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]\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]\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": 25000,
+    "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:f2ab039feebb1512b10e8cff124c80a6ee2c943d76f2b8ddebfa5fd3f4a4cea8
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1c98e267cdceeeb2d3d6ac19c73c1106295d5949b2d5f45fc7586e83a3f00748
+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: 1.21.6
+- 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 0x7f7fca316700>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f7fca313570>"}, "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": 8, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1676739829098334037, "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": "[[0.41732416 0.00791462 0.58068067]\n [0.41732416 0.00791462 0.58068067]\n [0.41732416 0.00791462 0.58068067]\n [0.41732416 0.00791462 0.58068067]\n [0.41732416 0.00791462 0.58068067]\n [0.41732416 0.00791462 0.58068067]\n [0.41732416 0.00791462 0.58068067]\n [0.41732416 0.00791462 0.58068067]]", "desired_goal": "[[-0.65799886  1.2734443   1.6655196 ]\n [ 0.43676153  1.4729974   0.16987438]\n [-0.68495524 -1.0240532   0.3629211 ]\n [ 0.9279082   1.6185586  -0.12941086]\n [-0.2037571   1.0735953  -1.5004973 ]\n [-0.58373564  1.0482644  -1.1853433 ]\n [-1.4133046   1.654495   -0.71168405]\n [ 0.40465677 -0.2935349  -1.0592175 ]]", "observation": "[[0.41732416 0.00791462 0.58068067 0.00358543 0.00154784 0.00344787]\n [0.41732416 0.00791462 0.58068067 0.00358543 0.00154784 0.00344787]\n [0.41732416 0.00791462 0.58068067 0.00358543 0.00154784 0.00344787]\n [0.41732416 0.00791462 0.58068067 0.00358543 0.00154784 0.00344787]\n [0.41732416 0.00791462 0.58068067 0.00358543 0.00154784 0.00344787]\n [0.41732416 0.00791462 0.58068067 0.00358543 0.00154784 0.00344787]\n [0.41732416 0.00791462 0.58068067 0.00358543 0.00154784 0.00344787]\n [0.41732416 0.00791462 0.58068067 0.00358543 0.00154784 0.00344787]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVewAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYIAAAAAAAAAAEBAQEBAQEBlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlC4="}, "_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]\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]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]", "desired_goal": "[[ 0.10893544  0.01306038  0.22715451]\n [ 0.02876914  0.08919743  0.06549251]\n [ 0.06211166 -0.0143438   0.15299623]\n [ 0.00295165  0.04722655  0.17781171]\n [ 0.04971906  0.04285478  0.06695668]\n [ 0.02347497  0.04196865  0.279613  ]\n [-0.04294747 -0.0660387   0.20341207]\n [-0.11268704  0.00961813  0.2935208 ]]", "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]\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]\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": 25000, "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": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.3787186849338468, "std_reward": 0.17081906002114203, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-18T17:47:22.127613"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e1cecddb19c1fbe197387d2c8a8cc7bec0243c634116537729a64976526e65ff
+size 3056