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: -0.17 +/- 0.10
+      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:ed594585fb83dc29cfecad336e2842c04ffa247c439858e0b553150b776e41f2
+size 108461

a2c-PandaReachDense-v3/_stable_baselines3_version

@@ -0,0 +1 @@
+2.1.0

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 0x7eaf052a2290>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7eaf052a52c0>"
+    },
+    "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": 1697092933054617788,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 0.28437945 -0.00300562  0.4395497 ]\n [-1.615477    1.3711406  -2.1957245 ]\n [ 0.28437945 -0.00300562  0.4395497 ]\n [ 0.28437945 -0.00300562  0.4395497 ]\n [ 0.28437945 -0.00300562  0.4395497 ]\n [ 0.28437945 -0.00300562  0.4395497 ]\n [-0.12309874 -0.45388457 -0.14417367]\n [ 0.28437945 -0.00300562  0.4395497 ]]",
+        "desired_goal": "[[-1.471687    0.668399   -1.4946615 ]\n [-0.68376535  1.1309063  -1.5034456 ]\n [-1.0961993   0.90372485  1.2759988 ]\n [-1.4523988  -0.9574057  -0.4786945 ]\n [-0.71805173  1.0119485  -1.4954485 ]\n [-0.76767874 -1.6099828  -0.4150477 ]\n [-0.6794811  -0.29541913  0.1020024 ]\n [ 1.1905935  -1.0314134  -0.68899995]]",
+        "observation": "[[ 0.28437945 -0.00300562  0.4395497   0.46437436 -0.00553083  0.3867247 ]\n [-1.615477    1.3711406  -2.1957245  -1.5697755  -0.8475615  -1.0226667 ]\n [ 0.28437945 -0.00300562  0.4395497   0.46437436 -0.00553083  0.3867247 ]\n [ 0.28437945 -0.00300562  0.4395497   0.46437436 -0.00553083  0.3867247 ]\n [ 0.28437945 -0.00300562  0.4395497   0.46437436 -0.00553083  0.3867247 ]\n [ 0.28437945 -0.00300562  0.4395497   0.46437436 -0.00553083  0.3867247 ]\n [-0.12309874 -0.45388457 -0.14417367 -1.5128452  -1.6669782  -1.210657  ]\n [ 0.28437945 -0.00300562  0.4395497   0.46437436 -0.00553083  0.3867247 ]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVewAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYIAAAAAAAAAAEAAQEBAQABlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////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": "[[ 0.03404305 -0.07558554  0.11149168]\n [ 0.12650372 -0.01414743  0.2566115 ]\n [-0.01324198 -0.11576455  0.03793562]\n [ 0.09134505 -0.13915265  0.01509801]\n [-0.09458639  0.00042282  0.1656366 ]\n [-0.12707649 -0.14704232  0.253707  ]\n [-0.09084181 -0.09736396  0.09351653]\n [-0.14803056  0.01560165  0.16652754]]",
+        "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,
+    "_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": 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,
+    "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": 8,
+    "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 sha256:366646a961c228cfdeb786e3c80058e102993dbaeb61faf564a0adf10a9d553e
+size 44606

a2c-PandaReachDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ba6a99d20b654ddd47e9572b96f19b01536e0ba310cb50c05106f56ba5561389
+size 45886

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-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.1.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: False
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.29.1
+- 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 0x7eaf052a2290>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7eaf052a52c0>"}, "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": 1697092933054617788, "learning_rate": 0.0007, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVewIAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QolmAAAAAAAAAAL5qRPuv5RLupDOE+88fOv4mBrz/AhgzAL5qRPuv5RLupDOE+L5qRPuv5RLupDOE+L5qRPuv5RLupDOE+L5qRPuv5RLupDOE+MRv8vY9j6L5DohO+L5qRPuv5RLupDOE+lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksISwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcolmAAAAAAAAAAPWC8vzIcKz8RUb+/Pwsvv4rBkD/ocMC/QlCMv4NaZz/uU6M/NOi5v4oYdb9yF/W+PdI3v4eHgT/bar+/mIZEv+sTzr8igdS+efItvy1Bl76j5tA9XmWYP1sFhL9NYjC/lGgOSwhLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWwAAAAAAAAAAvmpE+6/lEu6kM4T56wu0+/Du1u8gAxj7zx86/iYGvP8CGDMBn7si/yvlYv77mgr8vmpE+6/lEu6kM4T56wu0+/Du1u8gAxj4vmpE+6/lEu6kM4T56wu0+/Du1u8gAxj4vmpE+6/lEu6kM4T56wu0+/Du1u8gAxj4vmpE+6/lEu6kM4T56wu0+/Du1u8gAxj4xG/y9j2PovkOiE77ppMG/i1/Vv8/2mr8vmpE+6/lEu6kM4T56wu0+/Du1u8gAxj6UaA5LCEsGhpRoEnSUUpR1Lg==", "achieved_goal": "[[ 0.28437945 -0.00300562  0.4395497 ]\n [-1.615477    1.3711406  -2.1957245 ]\n [ 0.28437945 -0.00300562  0.4395497 ]\n [ 0.28437945 -0.00300562  0.4395497 ]\n [ 0.28437945 -0.00300562  0.4395497 ]\n [ 0.28437945 -0.00300562  0.4395497 ]\n [-0.12309874 -0.45388457 -0.14417367]\n [ 0.28437945 -0.00300562  0.4395497 ]]", "desired_goal": "[[-1.471687    0.668399   -1.4946615 ]\n [-0.68376535  1.1309063  -1.5034456 ]\n [-1.0961993   0.90372485  1.2759988 ]\n [-1.4523988  -0.9574057  -0.4786945 ]\n [-0.71805173  1.0119485  -1.4954485 ]\n [-0.76767874 -1.6099828  -0.4150477 ]\n [-0.6794811  -0.29541913  0.1020024 ]\n [ 1.1905935  -1.0314134  -0.68899995]]", "observation": "[[ 0.28437945 -0.00300562  0.4395497   0.46437436 -0.00553083  0.3867247 ]\n [-1.615477    1.3711406  -2.1957245  -1.5697755  -0.8475615  -1.0226667 ]\n [ 0.28437945 -0.00300562  0.4395497   0.46437436 -0.00553083  0.3867247 ]\n [ 0.28437945 -0.00300562  0.4395497   0.46437436 -0.00553083  0.3867247 ]\n [ 0.28437945 -0.00300562  0.4395497   0.46437436 -0.00553083  0.3867247 ]\n [ 0.28437945 -0.00300562  0.4395497   0.46437436 -0.00553083  0.3867247 ]\n [-0.12309874 -0.45388457 -0.14417367 -1.5128452  -1.6669782  -1.210657  ]\n [ 0.28437945 -0.00300562  0.4395497   0.46437436 -0.00553083  0.3867247 ]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVewAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYIAAAAAAAAAAEAAQEBAQABlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////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": "[[ 0.03404305 -0.07558554  0.11149168]\n [ 0.12650372 -0.01414743  0.2566115 ]\n [-0.01324198 -0.11576455  0.03793562]\n [ 0.09134505 -0.13915265  0.01509801]\n [-0.09458639  0.00042282  0.1656366 ]\n [-0.12707649 -0.14704232  0.253707  ]\n [-0.09084181 -0.09736396  0.09351653]\n [-0.14803056  0.01560165  0.16652754]]", "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, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWV4AsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHv9TKRuCPIXGMAWyUSwSMAXSUR0CmAinq3VkMdX2UKGgGR7/Kuh9LHuJDaAdLA2gIR0CmBS+BH09RdX2UKGgGR7/aSX+l0o0AaAdLBWgIR0CmCB2jGkvcdX2UKGgGR7/DV7x/d69kaAdLAmgIR0CmBtfa6BiDdX2UKGgGR7+5y4nWrfcfaAdLAmgIR0CmAxcDbJwLdX2UKGgGR7/M/zJ6po9LaAdLA2gIR0CmB6eCsfaIdX2UKGgGR7/JxZMcp9ZzaAdLA2gIR0CmBif9xZMddX2UKGgGR7/ANOM2m52AaAdLAmgIR0CmBUNOuaF3dX2UKGgGR7/OyBTXJ5miaAdLA2gIR0CmBBPr4WUKdX2UKGgGR7++M3qAz544aAdLAmgIR0CmBuuv+wTudX2UKGgGR7/X3Sa3I+4caAdLBGgIR0CmAlDWbwz+dX2UKGgGR7+mozeoDPnkaAdLAWgIR0CmBvdHDrJKdX2UKGgGR7/JGPPszEaVaAdLA2gIR0CmB8ZdGAkLdX2UKGgGR7/SiQDFId2gaAdLA2gIR0CmBWGtQsPKdX2UKGgGR7/IjO9nK4hEaAdLA2gIR0CmBDJMg2ZRdX2UKGgGR7/azFMqSX+maAdLBGgIR0CmA0Ajps42dX2UKGgGR7+zt6X0Gu9waAdLAmgIR0CmAmcDSw4bdX2UKGgGR7/aTdtVJcxCaAdLBGgIR0CmBlGucMEzdX2UKGgGR7/BWwNb1RLsaAdLAmgIR0CmBXXw1BMSdX2UKGgGR7+/4sVclgMMaAdLAmgIR0CmA1OieumrdX2UKGgGR7+5gRbr1M/RaAdLAmgIR0CmAno2XLNfdX2UKGgGR7/bKujh1klNaAdLB2gIR0CmCGRgy/KydX2UKGgGR7/OEBbOeJ53aAdLA2gIR0CmB+RQaaTfdX2UKGgGR7/geh4+r2g4aAdLBGgIR0CmBx6MR6F/dX2UKGgGR7+/wH7gsK9gaAdLAmgIR0CmBmTMA3kxdX2UKGgGR7/LGoaUA1ejaAdLA2gIR0CmBFBLGrCFdX2UKGgGR7+n+CK77Kq5aAdLAWgIR0CmA13rdFfBdX2UKGgGR7+nvphWo3rEaAdLAWgIR0CmA2lqBVdYdX2UKGgGR7/F0q6OHWSVaAdLA2gIR0CmBZTR6WxAdX2UKGgGR7/Aef7JnxrjaAdLAmgIR0CmBGVyeZogdX2UKGgGR7/Ljd56dDpkaAdLA2gIR0CmApljEvTPdX2UKGgGR7/I2kzoEB8yaAdLA2gIR0CmCIOCPIXCdX2UKGgGR7/Pz8P4EfT1aAdLA2gIR0CmCANvn8sMdX2UKGgGR7/O5n13+uNhaAdLA2gIR0CmBz20qpcYdX2UKGgGR7/RJj2Bas6raAdLA2gIR0CmBoQOOKfndX2UKGgGR7++2iL2pQ1raAdLAmgIR0CmBHfAsTWYdX2UKGgGR7+6zZ6D5CWvaAdLAmgIR0CmBpUIsyzpdX2UKGgGR7/UDoyKvV3EaAdLA2gIR0CmBbBRIjGDdX2UKGgGR7/L9If8uSOjaAdLA2gIR0CmArR3mmtRdX2UKGgGR7/V2VVxS5y3aAdLA2gIR0CmCKDtgKF7dX2UKGgGR7/M1Gb1AZ88aAdLA2gIR0CmCCEPDpC8dX2UKGgGR7/N5ULlV94NaAdLA2gIR0CmB1tpmEoOdX2UKGgGR7++oJiRW912aAdLAmgIR0CmBI0lqrR0dX2UKGgGR7+/aTOgQHzIaAdLAmgIR0CmBqySmqHXdX2UKGgGR7/BON5t3wCsaAdLAmgIR0CmAsuY6XBydX2UKGgGR7/N09yLhrFgaAdLA2gIR0CmBdBq9GqhdX2UKGgGR7/CMEzO5avBaAdLAmgIR0CmBKD9n9NvdX2UKGgGR7/KkvboKUmlaAdLA2gIR0CmCD5DZ13ddX2UKGgGR7/GyVObiIcjaAdLA2gIR0CmB3iVbA1vdX2UKGgGR7/Mcf/3nIQwaAdLA2gIR0CmBsevIOpbdX2UKGgGR7/mpmmLtNSJaAdLCWgIR0CmA8DAi3XqdX2UKGgGR7/OfDDTBqKxaAdLA2gIR0CmAudSl3yJdX2UKGgGR7/gSksSTQmeaAdLBWgIR0CmCNPAO8TSdX2UKGgGR7/HwXqJMxoJaAdLA2gIR0CmBL8biqACdX2UKGgGR7/JkcS5AhStaAdLA2gIR0CmCFw7DEWJdX2UKGgGR7/XJeE7GNrCaAdLBGgIR0CmBff0NBnjdX2UKGgGR7/a0u14Pf8/aAdLBGgIR0CmB59jwx33dX2UKGgGR7/VQ1JlJ6IFaAdLA2gIR0CmBuXxFy7xdX2UKGgGR7+6J0nw5NoKaAdLAmgIR0CmBNFzMibEdX2UKGgGR7/QeEqUeMhpaAdLA2gIR0CmAwUSh8IBdX2UKGgGR7/Utsenyd4FaAdLA2gIR0CmCO9weeWfdX2UKGgGR7+AS8J2MbWFaAdLAWgIR0CmBu+cx0uEdX2UKGgGR7+x7qptJnQIaAdLAmgIR0CmBgrHdXT3dX2UKGgGR7+iYgJTl1bJaAdLAWgIR0CmBNtHpbD/dX2UKGgGR7/WBomG/N7jaAdLBGgIR0CmA+iHymQ9dX2UKGgGR7/UCOFQEZBLaAdLA2gIR0CmCHjArQPadX2UKGgGR7+02bXpW3jNaAdLAmgIR0CmB7MdLg4wdX2UKGgGR7/D/KhcqvvCaAdLAmgIR0CmBPPZqVQidX2UKGgGR7+xd1MdtEXtaAdLAmgIR0CmBAFG5MDfdX2UKGgGR7/SO0LMLWqcaAdLA2gIR0CmAyfXGwRodX2UKGgGR7/Je9i+cpb2aAdLA2gIR0CmCRJj+aScdX2UKGgGR7+8bBGhEjPfaAdLAmgIR0CmB8x82JizdX2UKGgGR7/BkQwsXizcaAdLA2gIR0CmBxLKvFFVdX2UKGgGR7/Ct9x6v7m/aAdLA2gIR0CmBi4oAn2JdX2UKGgGR7/MVbA1vVEvaAdLA2gIR0CmCJxCQcPwdX2UKGgGR7+3A9FF2FFlaAdLAmgIR0CmBQfjCHh1dX2UKGgGR7/Ag/1QIldDaAdLAmgIR0CmBBVW8yvcdX2UKGgGR7+lRvWH1vl2aAdLAWgIR0CmCKVtwaR7dX2UKGgGR7/Q9FWn0kGBaAdLA2gIR0CmA0SOq//OdX2UKGgGR7/MQU5+6RQraAdLA2gIR0CmCS7qptJndX2UKGgGR7/N1rZamoBJaAdLA2gIR0CmB+j6vaDgdX2UKGgGR7/KflIVdonKaAdLA2gIR0CmBy9rXUYsdX2UKGgGR7/TJHAh0QsgaAdLA2gIR0CmBkqesgdPdX2UKGgGR7+4N/e+Eh7maAdLAmgIR0CmBRvBBRhudX2UKGgGR7/QZbILgGbDaAdLA2gIR0CmBDPJq7AddX2UKGgGR7+1v0h/y5I6aAdLAmgIR0CmA1qO1fE5dX2UKGgGR7/UM5OrQw9JaAdLA2gIR0CmCMR0uDjBdX2UKGgGR7/C814xDb8FaAdLAmgIR0CmB/6+vhZRdX2UKGgGR7+8Sf16E8JVaAdLAmgIR0CmBmABcRlIdX2UKGgGR7/PnaFmFrVOaAdLA2gIR0CmCU5jpcHGdX2UKGgGR7/Iqx1PnB+GaAdLA2gIR0CmB05o4+8odX2UKGgGR7/OOVgQYk3TaAdLA2gIR0CmBToTwlSkdX2UKGgGR7/DGFSKm8/VaAdLAmgIR0CmA22bwz+FdX2UKGgGR7/E/O+qR2bHaAdLA2gIR0CmBFCrDIikdX2UKGgGR7+ydjG1hLGraAdLAmgIR0CmCWEMTewcdX2UKGgGR7/Sq/M4cWCVaAdLA2gIR0CmCBr6+FlDdX2UKGgGR7+6n/DLr5ZbaAdLAmgIR0CmB2Far3j/dX2UKGgGR7/Gp6yB06o3aAdLA2gIR0CmBnyPMjeLdX2UKGgGR7/CQEpy6tknaAdLAmgIR0CmBU0xVQyidX2UKGgGR7/AD7Ikqto0aAdLAmgIR0CmA4DFQ2uQdWUu"}, "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, "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": 8, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.1.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "False", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.29.1", "OpenAI Gym": "0.25.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.172893495997414, "std_reward": 0.10094855105862631, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-10-12T07:37:43.562742"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7c985df29771ac1dd6dd057227e8195af433b596716f899ba3a71bdcd31e860c
+size 2648