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: -1.40 +/- 0.12
+      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 sha256:4721fc0a8f8eec687f438a842cf7f79755410befb16c2ee46fff30fcde42eb2e
+size 108060

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 0x7f3d92114a60>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f3d920fbbc0>"
+    },
+    "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": 100000,
+    "_total_timesteps": 100000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1685362740981857206,
+    "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": "[[ 0.2775171 -0.022022   0.5009889]\n [ 0.2775171 -0.022022   0.5009889]\n [ 0.2775171 -0.022022   0.5009889]\n [ 0.2775171 -0.022022   0.5009889]]",
+        "desired_goal": "[[-0.51688385 -0.6379008  -1.5451399 ]\n [-0.9317217  -1.5127714  -0.63511777]\n [ 0.28187048 -0.5417327  -1.1689161 ]\n [-0.25967085  0.6292676  -0.26643753]]",
+        "observation": "[[ 0.2775171  -0.022022    0.5009889  -0.00122725 -0.00180852  0.01409084]\n [ 0.2775171  -0.022022    0.5009889  -0.00122725 -0.00180852  0.01409084]\n [ 0.2775171  -0.022022    0.5009889  -0.00122725 -0.00180852  0.01409084]\n [ 0.2775171  -0.022022    0.5009889  -0.00122725 -0.00180852  0.01409084]]"
+    },
+    "_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": "[[-0.08033427  0.08910427  0.00359364]\n [ 0.08883515  0.07105843  0.1559294 ]\n [-0.07703256 -0.02851579  0.15222512]\n [ 0.0392666  -0.1232048   0.18859506]]",
+        "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": 5000,
+    "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:db4661f09a6103a5ee44d5374fe64f5e093db9f4990daa977fafc914f7b3af47
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f93b6c4b938162a682574f2560cbae319ed207b13266bae4881dde6a3807d939
+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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
+- Python: 3.10.11
+- 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 0x7f3d92114a60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f3d920fbbc0>"}, "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": 100000, "_total_timesteps": 100000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1685362740981857206, "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": "[[ 0.2775171 -0.022022   0.5009889]\n [ 0.2775171 -0.022022   0.5009889]\n [ 0.2775171 -0.022022   0.5009889]\n [ 0.2775171 -0.022022   0.5009889]]", "desired_goal": "[[-0.51688385 -0.6379008  -1.5451399 ]\n [-0.9317217  -1.5127714  -0.63511777]\n [ 0.28187048 -0.5417327  -1.1689161 ]\n [-0.25967085  0.6292676  -0.26643753]]", "observation": "[[ 0.2775171  -0.022022    0.5009889  -0.00122725 -0.00180852  0.01409084]\n [ 0.2775171  -0.022022    0.5009889  -0.00122725 -0.00180852  0.01409084]\n [ 0.2775171  -0.022022    0.5009889  -0.00122725 -0.00180852  0.01409084]\n [ 0.2775171  -0.022022    0.5009889  -0.00122725 -0.00180852  0.01409084]]"}, "_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": "[[-0.08033427  0.08910427  0.00359364]\n [ 0.08883515  0.07105843  0.1559294 ]\n [-0.07703256 -0.02851579  0.15222512]\n [ 0.0392666  -0.1232048   0.18859506]]", "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////9LAHSUYkMI8lt0stT6+b+UhpRSlIwBbJRLMowBdJRHQG90dIGyHEd1fZQoaAZoCWgPQwhwzojS3mD2v5SGlFKUaBVLMmgWR0Bvb+CCjDbbdX2UKGgGaAloD0MIixagbTVr+r+UhpRSlGgVSzJoFkdAb2wHN5dGAnV9lChoBmgJaA9DCNTUsrW+iP+/lIaUUpRoFUsyaBZHQG9oLux8lX11fZQoaAZoCWgPQwi9b3ztmSX3v5SGlFKUaBVLMmgWR0Bvg5tDUmUodX2UKGgGaAloD0MIF9nO91Nj87+UhpRSlGgVSzJoFkdAb38HKwIMSnV9lChoBmgJaA9DCBtLWBtjJ/y/lIaUUpRoFUsyaBZHQG97Kxkd3jd1fZQoaAZoCWgPQwi4ByEgXwL8v5SGlFKUaBVLMmgWR0Bvd1ObiIcjdX2UKGgGaAloD0MIBARz9Pg9/7+UhpRSlGgVSzJoFkdAb5LftQbdanV9lChoBmgJaA9DCKm8HeG04Pm/lIaUUpRoFUsyaBZHQG+OUrkKeCl1fZQoaAZoCWgPQwh1q+ek9034v5SGlFKUaBVLMmgWR0BvinUSZjQRdX2UKGgGaAloD0MICDpa1ZIO+L+UhpRSlGgVSzJoFkdAb4adupCKJnV9lChoBmgJaA9DCC9SKAtfX/i/lIaUUpRoFUsyaBZHQG+hXKbKA8V1fZQoaAZoCWgPQwhW16Gakmz4v5SGlFKUaBVLMmgWR0BvnMgIQe3hdX2UKGgGaAloD0MI9IsS9Bc687+UhpRSlGgVSzJoFkdAb5joIOYplXV9lChoBmgJaA9DCANf0a3XtPi/lIaUUpRoFUsyaBZHQG+VD9Oymhx1fZQoaAZoCWgPQwhqhH6mXvf7v5SGlFKUaBVLMmgWR0Bvr1l2/zredX2UKGgGaAloD0MIZ5qw/WTM97+UhpRSlGgVSzJoFkdAb6rFrl/6PHV9lChoBmgJaA9DCGYzh6QWSva/lIaUUpRoFUsyaBZHQG+m5l4C6pZ1fZQoaAZoCWgPQwiAft+/eXH3v5SGlFKUaBVLMmgWR0BvowuIyj59dX2UKGgGaAloD0MIbXNjesKS+L+UhpRSlGgVSzJoFkdAb72ZYPoV23V9lChoBmgJaA9DCHrhzoWRXv2/lIaUUpRoFUsyaBZHQG+5A5Jbt7d1fZQoaAZoCWgPQwjDDmPS38v6v5SGlFKUaBVLMmgWR0BvtSNdZ7ojdX2UKGgGaAloD0MIBWoxeJi2/b+UhpRSlGgVSzJoFkdAb7FIbwSamXV9lChoBmgJaA9DCDrq6Lgamfu/lIaUUpRoFUsyaBZHQG/S7LEDQqt1fZQoaAZoCWgPQwi/YaJBCt75v5SGlFKUaBVLMmgWR0BvzmEZiuuBdX2UKGgGaAloD0MIsVBrmndc+7+UhpRSlGgVSzJoFkdAb8qL9/BnBnV9lChoBmgJaA9DCGoTJ/c7FPy/lIaUUpRoFUsyaBZHQG/Gwuuieup1fZQoaAZoCWgPQwjHgy12+2z6v5SGlFKUaBVLMmgWR0Bv6k3Kji4sdX2UKGgGaAloD0MIwFyLFqDt9r+UhpRSlGgVSzJoFkdAb+XEiMYMv3V9lChoBmgJaA9DCPGhREseT/m/lIaUUpRoFUsyaBZHQG/h9NnGsFN1fZQoaAZoCWgPQwgc0T3rGi38v5SGlFKUaBVLMmgWR0Bv3iOPvKEGdX2UKGgGaAloD0MIc58cBYjC97+UhpRSlGgVSzJoFkdAcADhlUZNwnV9lChoBmgJaA9DCHE7NCxGnfW/lIaUUpRoFUsyaBZHQG/9Ok+HJtB1fZQoaAZoCWgPQwgKFLGIYQf3v5SGlFKUaBVLMmgWR0Bv+WitaIN3dX2UKGgGaAloD0MIR+NQvwsb/r+UhpRSlGgVSzJoFkdAb/WWbgCOm3V9lChoBmgJaA9DCMXL07miFPi/lIaUUpRoFUsyaBZHQHAM/Uaya/h1fZQoaAZoCWgPQwimJyzxgDL5v5SGlFKUaBVLMmgWR0BwCrn5i3G5dX2UKGgGaAloD0MILEfIQJ6d9r+UhpRSlGgVSzJoFkdAcAjQpWmxdXV9lChoBmgJaA9DCG/0MR8QKAHAlIaUUpRoFUsyaBZHQHAG6FuejEh1fZQoaAZoCWgPQwhWfa62Yn/3v5SGlFKUaBVLMmgWR0BwGQsjFAE/dX2UKGgGaAloD0MI9dvXgXMG9b+UhpRSlGgVSzJoFkdAcBbFkhA4XHV9lChoBmgJaA9DCFNCsKpevvy/lIaUUpRoFUsyaBZHQHAU2v8qFyt1fZQoaAZoCWgPQwiU9ZuJ6QL6v5SGlFKUaBVLMmgWR0BwEvOTq0MPdX2UKGgGaAloD0MI4lZBDHRt+r+UhpRSlGgVSzJoFkdAcCU7gKnei3V9lChoBmgJaA9DCEgZcQFoVPi/lIaUUpRoFUsyaBZHQHAi9hd+ocd1fZQoaAZoCWgPQwiW6ZeIt476v5SGlFKUaBVLMmgWR0BwIQvUSZjQdX2UKGgGaAloD0MIk2+2uTF9+7+UhpRSlGgVSzJoFkdAcB8lQ/HHWHV9lChoBmgJaA9DCIUIOIQqtfa/lIaUUpRoFUsyaBZHQHAxMWTHKfZ1fZQoaAZoCWgPQwiSlPQwtDoCwJSGlFKUaBVLMmgWR0BwLutMfzSUdX2UKGgGaAloD0MIiSZQxCKG+r+UhpRSlGgVSzJoFkdAcC0CuEEkjXV9lChoBmgJaA9DCDS6g9iZQve/lIaUUpRoFUsyaBZHQHArHtShrWR1fZQoaAZoCWgPQwge/pqsUY/1v5SGlFKUaBVLMmgWR0BwPWdf9gnddX2UKGgGaAloD0MIfVuwVBew9r+UhpRSlGgVSzJoFkdAcDshEBsAN3V9lChoBmgJaA9DCIWzW8tkePm/lIaUUpRoFUsyaBZHQHA5NWhh6Sl1fZQoaAZoCWgPQwha8Q2Fz9b+v5SGlFKUaBVLMmgWR0BwN0zWPLgXdX2UKGgGaAloD0MIy7p/LERH9L+UhpRSlGgVSzJoFkdAcESpT/ACXHV9lChoBmgJaA9DCNl6hnDMMvi/lIaUUpRoFUsyaBZHQHBCYB7u2JB1fZQoaAZoCWgPQwh+4ZUkzzX7v5SGlFKUaBVLMmgWR0BwQHAuZkTYdX2UKGgGaAloD0MIZ0P+mUH89b+UhpRSlGgVSzJoFkdAcD6DPGACn3V9lChoBmgJaA9DCPuQt1z9mPi/lIaUUpRoFUsyaBZHQHBLi8jAzpJ1fZQoaAZoCWgPQwiGjbJ+MzH7v5SGlFKUaBVLMmgWR0BwSUHfMwDedX2UKGgGaAloD0MIy4P0FDmE/L+UhpRSlGgVSzJoFkdAcEdS8an753V9lChoBmgJaA9DCMizy7c+LPa/lIaUUpRoFUsyaBZHQHBFZ3kgfU51fZQoaAZoCWgPQwiZ1NAGYAP6v5SGlFKUaBVLMmgWR0BwUsikfs/qdX2UKGgGaAloD0MISfdzCvKz97+UhpRSlGgVSzJoFkdAcFB9uxbB43V9lChoBmgJaA9DCC7kEdxImfm/lIaUUpRoFUsyaBZHQHBOjwUg0TF1fZQoaAZoCWgPQwh/2T15WOj3v5SGlFKUaBVLMmgWR0BwTKHWSU1RdX2UKGgGaAloD0MIs9DOaRZo+r+UhpRSlGgVSzJoFkdAcFnRJ2+wknV9lChoBmgJaA9DCJHQlnMp7v6/lIaUUpRoFUsyaBZHQHBXikfs/pt1fZQoaAZoCWgPQwiIZwkyAmr+v5SGlFKUaBVLMmgWR0BwVaKqGUOedX2UKGgGaAloD0MI+imOA6/W+L+UhpRSlGgVSzJoFkdAcFO495hScnV9lChoBmgJaA9DCPD5YYTwqP2/lIaUUpRoFUsyaBZHQHBg5W/8EV51fZQoaAZoCWgPQwiYvWw7bc35v5SGlFKUaBVLMmgWR0BwXpqWTot+dX2UKGgGaAloD0MIHAqfrYOD+b+UhpRSlGgVSzJoFkdAcFyqmj0tiHV9lChoBmgJaA9DCO7p6o7Ftva/lIaUUpRoFUsyaBZHQHBavkaMrEt1fZQoaAZoCWgPQwg42nHD7+YAwJSGlFKUaBVLMmgWR0BwZ8js2NvPdX2UKGgGaAloD0MIIsK/CBpz/b+UhpRSlGgVSzJoFkdAcGV+YtxuK3V9lChoBmgJaA9DCKPlQA+1rfe/lIaUUpRoFUsyaBZHQHBjjewcHW11fZQoaAZoCWgPQwgH6pRHNwL+v5SGlFKUaBVLMmgWR0BwYaDh99c9dX2UKGgGaAloD0MI6L6c2a7Q+r+UhpRSlGgVSzJoFkdAcG7a1TisGXV9lChoBmgJaA9DCPHxCdl52/+/lIaUUpRoFUsyaBZHQHBskCaJAMV1fZQoaAZoCWgPQwiSskXSbrT5v5SGlFKUaBVLMmgWR0BwaqBz3h4udX2UKGgGaAloD0MI6nk3FhQG+7+UhpRSlGgVSzJoFkdAcGizT4L1EnV9lChoBmgJaA9DCIoFvqJbL/q/lIaUUpRoFUsyaBZHQHB2EmhM8HR1fZQoaAZoCWgPQwifW+hKBGr2v5SGlFKUaBVLMmgWR0Bwc8cxTKkmdX2UKGgGaAloD0MI+l+uRQsQ97+UhpRSlGgVSzJoFkdAcHHXVLBbfXV9lChoBmgJaA9DCD0K16Nwffm/lIaUUpRoFUsyaBZHQHBv6be/Ho51fZQoaAZoCWgPQwiM9nghHd75v5SGlFKUaBVLMmgWR0BwfTFYMfA9dX2UKGgGaAloD0MIv/G1Z5YE+L+UhpRSlGgVSzJoFkdAcHrof0VafXV9lChoBmgJaA9DCKZEEr2M4vq/lIaUUpRoFUsyaBZHQHB4+oP07Kd1fZQoaAZoCWgPQwiB6h9EMiT/v5SGlFKUaBVLMmgWR0Bwdw5myxA0dX2UKGgGaAloD0MITdpU3SMb/r+UhpRSlGgVSzJoFkdAcISPrv9cbHV9lChoBmgJaA9DCAPQKF36F/a/lIaUUpRoFUsyaBZHQHCCRZ+x4Y91fZQoaAZoCWgPQwh9y5wuiwn3v5SGlFKUaBVLMmgWR0BwgFd2Pkq+dX2UKGgGaAloD0MI8GyP3nAf+7+UhpRSlGgVSzJoFkdAcH5rgwXZXnV9lChoBmgJaA9DCMBbIEHx4/e/lIaUUpRoFUsyaBZHQHCLuzQeFL51fZQoaAZoCWgPQwhcdLLUen/1v5SGlFKUaBVLMmgWR0BwiXBfrrxBdX2UKGgGaAloD0MI1/uNdtxw+b+UhpRSlGgVSzJoFkdAcIeAO8TSLXV9lChoBmgJaA9DCPBS6pJxDPi/lIaUUpRoFUsyaBZHQHCFkwrUb1h1ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 5000, "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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.11", "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": -1.3982892724219709, "std_reward": 0.1152393186625247, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-05-29T12:24:08.789483"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:223617c8b7bee1832161889954cc8125e65a1a52970e51ac80ccafd4b672e1f4
+size 2387