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: -0.51 +/- 0.19
+      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 sha256:68b52c55fa3c54e8d9e482a8de75b28aa6b8fed05bca1ef4653d339708d714b0
+size 108157

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 0x793d7d2fd6c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x793d7d3085c0>"
+    },
+    "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": 1690659880900361855,
+    "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": "[[ 0.33721477 -0.00194629  0.56028545]\n [ 0.33721477 -0.00194629  0.56028545]\n [ 0.33721477 -0.00194629  0.56028545]\n [ 0.33721477 -0.00194629  0.56028545]]",
+        "desired_goal": "[[ 0.11840488  0.8178329   1.6015438 ]\n [-0.8596969   0.88509184  0.26264158]\n [ 0.5172282  -1.6891214   0.2011727 ]\n [-0.12308685  1.2584481  -0.85600525]]",
+        "observation": "[[ 3.3721477e-01 -1.9462862e-03  5.6028545e-01  1.3557392e-02\n  -1.9020066e-04  1.1186357e-02]\n [ 3.3721477e-01 -1.9462862e-03  5.6028545e-01  1.3557392e-02\n  -1.9020066e-04  1.1186357e-02]\n [ 3.3721477e-01 -1.9462862e-03  5.6028545e-01  1.3557392e-02\n  -1.9020066e-04  1.1186357e-02]\n [ 3.3721477e-01 -1.9462862e-03  5.6028545e-01  1.3557392e-02\n  -1.9020066e-04  1.1186357e-02]]"
+    },
+    "_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": "[[-0.14331277  0.11464673  0.02734557]\n [-0.09649035 -0.11775575  0.07166234]\n [ 0.0832554  -0.0729411   0.20747228]\n [-0.06587096  0.03303692  0.08351396]]",
+        "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 sha256:82740918c7cd3f59b6826a4794c470968d18c713a9164a2881baa201cd72c7d6
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3ce1e4be89f13d1a4fa6e28007f45158e331737521f0a7fac61f0954422bce94
+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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.6
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.1+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 0x793d7d2fd6c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x793d7d3085c0>"}, "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": 1690659880900361855, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAaqesPoca/7rebg8/aqesPoca/7rebg8/aqesPoca/7rebg8/aqesPoca/7rebg8/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAQn7yPX9dUT9j/8w/GRVcv2GVYj/1eIY+EWkEPyE12L83AE4+9RT8vdQUoT8pI1u/lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAABqp6w+hxr/ut5uDz/TH148mnBHuftGNzxqp6w+hxr/ut5uDz/TH148mnBHuftGNzxqp6w+hxr/ut5uDz/TH148mnBHuftGNzxqp6w+hxr/ut5uDz/TH148mnBHuftGNzyUaA5LBEsGhpRoEnSUUpR1Lg==", "achieved_goal": "[[ 0.33721477 -0.00194629  0.56028545]\n [ 0.33721477 -0.00194629  0.56028545]\n [ 0.33721477 -0.00194629  0.56028545]\n [ 0.33721477 -0.00194629  0.56028545]]", "desired_goal": "[[ 0.11840488  0.8178329   1.6015438 ]\n [-0.8596969   0.88509184  0.26264158]\n [ 0.5172282  -1.6891214   0.2011727 ]\n [-0.12308685  1.2584481  -0.85600525]]", "observation": "[[ 3.3721477e-01 -1.9462862e-03  5.6028545e-01  1.3557392e-02\n  -1.9020066e-04  1.1186357e-02]\n [ 3.3721477e-01 -1.9462862e-03  5.6028545e-01  1.3557392e-02\n  -1.9020066e-04  1.1186357e-02]\n [ 3.3721477e-01 -1.9462862e-03  5.6028545e-01  1.3557392e-02\n  -1.9020066e-04  1.1186357e-02]\n [ 3.3721477e-01 -1.9462862e-03  5.6028545e-01  1.3557392e-02\n  -1.9020066e-04  1.1186357e-02]]"}, "_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////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAlcASvufL6j3PA+A8vJzFve0p8b20w5I9z4GqPSVilb2dc1Q+W+eGvblRBz1eCas9lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAADqch09GWwarEMjSj4AAAAAAAAAgAAAAACUaA5LBEsGhpRoEnSUUpR1Lg==", "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": "[[-0.14331277  0.11464673  0.02734557]\n [-0.09649035 -0.11775575  0.07166234]\n [ 0.0832554  -0.0729411   0.20747228]\n [-0.06587096  0.03303692  0.08351396]]", "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:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIach4lEp42b+UhpRSlIwBbJRLMowBdJRHQKZG3MmF8G91fZQoaAZoCWgPQwi9b3ztmSXiv5SGlFKUaBVLMmgWR0CmRp4DTz/ZdX2UKGgGaAloD0MIKNGSx9Pyzb+UhpRSlGgVSzJoFkdApkZcEkjX4HV9lChoBmgJaA9DCEZ9kjtsIuK/lIaUUpRoFUsyaBZHQKZGGT238XN1fZQoaAZoCWgPQwi3DaMgeHzov5SGlFKUaBVLMmgWR0CmR/ltbcGkdX2UKGgGaAloD0MIdCSX/5C+8L+UhpRSlGgVSzJoFkdApke6rT6SDHV9lChoBmgJaA9DCArXo3A9Ctu/lIaUUpRoFUsyaBZHQKZHeLCvX9R1fZQoaAZoCWgPQwjOqWQAqOLMv5SGlFKUaBVLMmgWR0CmRzW8AaNudX2UKGgGaAloD0MIgPEMGvon27+UhpRSlGgVSzJoFkdApkkbsF+uvHV9lChoBmgJaA9DCJBPyM7b2N6/lIaUUpRoFUsyaBZHQKZI3n+yZ8d1fZQoaAZoCWgPQwhBtixfl+HXv5SGlFKUaBVLMmgWR0CmSJ4Fiay9dX2UKGgGaAloD0MI93R1x2Kb7L+UhpRSlGgVSzJoFkdApkhb8+A3DXV9lChoBmgJaA9DCH7Er1jDRdS/lIaUUpRoFUsyaBZHQKZK+Mm4RVZ1fZQoaAZoCWgPQwiy2vy/6kjkv5SGlFKUaBVLMmgWR0CmSrrA57w8dX2UKGgGaAloD0MIOIHptG6D5b+UhpRSlGgVSzJoFkdApkp5bILgGnV9lChoBmgJaA9DCOuQm+EGfOq/lIaUUpRoFUsyaBZHQKZKN54W1tx1fZQoaAZoCWgPQwhCQ/8EFyviv5SGlFKUaBVLMmgWR0CmTLsir1dxdX2UKGgGaAloD0MI41MAjGfQ6r+UhpRSlGgVSzJoFkdApkx9Jtix3XV9lChoBmgJaA9DCKTjamRX2ui/lIaUUpRoFUsyaBZHQKZMO9A5aNd1fZQoaAZoCWgPQwg3UUtzKwTpv5SGlFKUaBVLMmgWR0CmS/mp2ll9dX2UKGgGaAloD0MIwr8IGjMJ87+UhpRSlGgVSzJoFkdApk6inYQJ5XV9lChoBmgJaA9DCKyql99pMti/lIaUUpRoFUsyaBZHQKZOZPBzmwJ1fZQoaAZoCWgPQwhv9DEfEOjsv5SGlFKUaBVLMmgWR0CmTiQJPZZkdX2UKGgGaAloD0MIwQEtXcE217+UhpRSlGgVSzJoFkdApk3iMglniHV9lChoBmgJaA9DCL4wmSoYFeG/lIaUUpRoFUsyaBZHQKZQgbmU4aR1fZQoaAZoCWgPQwhkPiDQmbTWv5SGlFKUaBVLMmgWR0CmUEL/bTMJdX2UKGgGaAloD0MIgxd9BWlG5b+UhpRSlGgVSzJoFkdAplABJXhfjXV9lChoBmgJaA9DCG40gLdAAuG/lIaUUpRoFUsyaBZHQKZPvjQRf4R1fZQoaAZoCWgPQwgKvmn67IDjv5SGlFKUaBVLMmgWR0CmUaFcpsoEdX2UKGgGaAloD0MIPpXTnpJz2r+UhpRSlGgVSzJoFkdAplFjftQbdnV9lChoBmgJaA9DCJ1KBoAq7uS/lIaUUpRoFUsyaBZHQKZRIZk078x1fZQoaAZoCWgPQwgaiGUzh2Txv5SGlFKUaBVLMmgWR0CmUN6y0KJEdX2UKGgGaAloD0MIPwCpTZzc0r+UhpRSlGgVSzJoFkdAplLPgJkXlHV9lChoBmgJaA9DCHJsPUM4Zt2/lIaUUpRoFUsyaBZHQKZSkMir1dx1fZQoaAZoCWgPQwh7vma5bLTxv5SGlFKUaBVLMmgWR0CmUk+AuqWDdX2UKGgGaAloD0MIUmFsIchB37+UhpRSlGgVSzJoFkdAplIMlolD4XV9lChoBmgJaA9DCHrGvmTjweC/lIaUUpRoFUsyaBZHQKZUCXCTEBN1fZQoaAZoCWgPQwhTBg5o6Qrcv5SGlFKUaBVLMmgWR0CmU8rbg0j1dX2UKGgGaAloD0MI+Um1T8fj4L+UhpRSlGgVSzJoFkdAplOJEH+qBHV9lChoBmgJaA9DCBl0Quigy/W/lIaUUpRoFUsyaBZHQKZTRlo11nx1fZQoaAZoCWgPQwgRVmMJa2Pmv5SGlFKUaBVLMmgWR0CmVSY1P3zudX2UKGgGaAloD0MI/d07akzI8b+UhpRSlGgVSzJoFkdAplTna11GLHV9lChoBmgJaA9DCPlnBvGBXfO/lIaUUpRoFUsyaBZHQKZUpWbPQfJ1fZQoaAZoCWgPQwjM7zSZ8bbwv5SGlFKUaBVLMmgWR0CmVGKMm4RVdX2UKGgGaAloD0MI1sbYCS9B+r+UhpRSlGgVSzJoFkdAplZBtSAH3XV9lChoBmgJaA9DCFJflnZqrua/lIaUUpRoFUsyaBZHQKZWAydnTRZ1fZQoaAZoCWgPQwh+G2K85lXvv5SGlFKUaBVLMmgWR0CmVcFuNxVAdX2UKGgGaAloD0MIPbg7a7cd+r+UhpRSlGgVSzJoFkdAplV+mk30gHV9lChoBmgJaA9DCFSnA1lPrfa/lIaUUpRoFUsyaBZHQKZXV5prULF1fZQoaAZoCWgPQwjxvFRszCvwv5SGlFKUaBVLMmgWR0CmVxkd3jdYdX2UKGgGaAloD0MI9MMI4dFG+b+UhpRSlGgVSzJoFkdAplbXOD8Lr3V9lChoBmgJaA9DCHb6QV2kUOO/lIaUUpRoFUsyaBZHQKZWlE9+w1R1fZQoaAZoCWgPQwiQoWMHlfjyv5SGlFKUaBVLMmgWR0CmWGw71ZkkdX2UKGgGaAloD0MIniPyXUpd07+UhpRSlGgVSzJoFkdAplgtoakylHV9lChoBmgJaA9DCHzw2qUNh96/lIaUUpRoFUsyaBZHQKZX69eyAx11fZQoaAZoCWgPQwhvLv62J8jvv5SGlFKUaBVLMmgWR0CmV6kXDWK/dX2UKGgGaAloD0MIHQQdrWrJ47+UhpRSlGgVSzJoFkdAplmP+6y0KXV9lChoBmgJaA9DCFtCPujZLPC/lIaUUpRoFUsyaBZHQKZZUSCe2/l1fZQoaAZoCWgPQwiXUpeMY6Tmv5SGlFKUaBVLMmgWR0CmWQ8F6iTMdX2UKGgGaAloD0MI7Bfshm2L4L+UhpRSlGgVSzJoFkdApljL+kxh2HV9lChoBmgJaA9DCLHDmPT30v6/lIaUUpRoFUsyaBZHQKZanqrzXjF1fZQoaAZoCWgPQwibIVUUrzLtv5SGlFKUaBVLMmgWR0CmWl/9gnc+dX2UKGgGaAloD0MIrcJmgAuy5r+UhpRSlGgVSzJoFkdAploeDYh+v3V9lChoBmgJaA9DCCAJ+3YSEdq/lIaUUpRoFUsyaBZHQKZZ2zN2TxJ1fZQoaAZoCWgPQwg6yyxCsZXov5SGlFKUaBVLMmgWR0CmW7ykKu0UdX2UKGgGaAloD0MI68cm+RE/+b+UhpRSlGgVSzJoFkdAplt9+NLlFXV9lChoBmgJaA9DCLd6Tnrf+O6/lIaUUpRoFUsyaBZHQKZbPAYYR/V1fZQoaAZoCWgPQwhtcY3PZP/kv5SGlFKUaBVLMmgWR0CmWvkfkmx/dX2UKGgGaAloD0MIba6a54h837+UhpRSlGgVSzJoFkdAplzMkMTewnV9lChoBmgJaA9DCHrIlA9B1de/lIaUUpRoFUsyaBZHQKZcjiIcinp1fZQoaAZoCWgPQwhZbJOKxlrmv5SGlFKUaBVLMmgWR0CmXExtP558dX2UKGgGaAloD0MIis3HtaFi27+UhpRSlGgVSzJoFkdAplwKUFB6bHV9lChoBmgJaA9DCHb/WIgOQfO/lIaUUpRoFUsyaBZHQKZd5dhy8z11fZQoaAZoCWgPQwjtDb4wmarhv5SGlFKUaBVLMmgWR0CmXacnuy/sdX2UKGgGaAloD0MI6uv5muWy5L+UhpRSlGgVSzJoFkdApl1lOARTTHV9lChoBmgJaA9DCAn6Cz1iNPO/lIaUUpRoFUsyaBZHQKZdIk7fYSR1fZQoaAZoCWgPQwj6gEBn0mbxv5SGlFKUaBVLMmgWR0CmXv+sPrfMdX2UKGgGaAloD0MIgV64c2Ek5b+UhpRSlGgVSzJoFkdApl7A93bEgnV9lChoBmgJaA9DCJWfVPt0vOS/lIaUUpRoFUsyaBZHQKZefv/io891fZQoaAZoCWgPQwihSPdzCvLXv5SGlFKUaBVLMmgWR0CmXjwsoUi7dX2UKGgGaAloD0MIBRps6jwq47+UhpRSlGgVSzJoFkdApmAZnL7oCHV9lChoBmgJaA9DCHsUrkfheum/lIaUUpRoFUsyaBZHQKZf2t/WlM11fZQoaAZoCWgPQwh7o1aYvlfgv5SGlFKUaBVLMmgWR0CmX5jopx3ndX2UKGgGaAloD0MIaFpiZTTy37+UhpRSlGgVSzJoFkdApl9V/Ue+23V9lChoBmgJaA9DCP9Z8+MvLdm/lIaUUpRoFUsyaBZHQKZhLg4Otnx1fZQoaAZoCWgPQwjr4GBvYkjpv5SGlFKUaBVLMmgWR0CmYO8+iaiLdX2UKGgGaAloD0MIkwILYMrA5L+UhpRSlGgVSzJoFkdApmCtPJq7AnV9lChoBmgJaA9DCN2WyAVncOe/lIaUUpRoFUsyaBZHQKZgakyk9EF1fZQoaAZoCWgPQwhUrYVZaOfcv5SGlFKUaBVLMmgWR0CmYkBS1maqdX2UKGgGaAloD0MIqkTZW8r51r+UhpRSlGgVSzJoFkdApmIBgAp8W3V9lChoBmgJaA9DCFQAjGfQ0NW/lIaUUpRoFUsyaBZHQKZhv2alUId1fZQoaAZoCWgPQwim0k84uzXlv5SGlFKUaBVLMmgWR0CmYXxh+fAcdX2UKGgGaAloD0MIxy5RvTWw5L+UhpRSlGgVSzJoFkdApmNSJAMUh3V9lChoBmgJaA9DCHnqkQa3teS/lIaUUpRoFUsyaBZHQKZjE21D0Dl1fZQoaAZoCWgPQwjxD1t6NFXmv5SGlFKUaBVLMmgWR0CmYtFmWdEtdX2UKGgGaAloD0MI5Eo9C0L56L+UhpRSlGgVSzJoFkdApmKOaQV9GHV9lChoBmgJaA9DCPbv+sxZH+S/lIaUUpRoFUsyaBZHQKZkaaFVT751fZQoaAZoCWgPQwjxRXu8kA7hv5SGlFKUaBVLMmgWR0CmZCrfk3judX2UKGgGaAloD0MIJXUCmgib5b+UhpRSlGgVSzJoFkdApmPo2bXpW3V9lChoBmgJaA9DCH+l8+FZgtq/lIaUUpRoFUsyaBZHQKZjpfE4vOB1ZS4="}, "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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.5050731600495055, "std_reward": 0.19077903727006867, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-29T20:32:31.442514"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cae6d2ac4ebf1e9a396fac67003cb0c5d5cd02b3671b887477b70857bb01af25
+size 2387