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: -3.28 +/- 1.25
+      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 sha256:03ea1e3536c3dd01ea5781930ab5f2a4ae68412ca65a83d2d563dd95944bb1a9
+size 108028

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 0x7f3dcbbb01f0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f3dcbbace40>"
+    },
+    "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": 4,
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1678896586649900642,
+    "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": "[[ 0.39228433 -0.01627604  0.54875505]\n [ 0.39228433 -0.01627604  0.54875505]\n [ 0.39228433 -0.01627604  0.54875505]\n [ 0.39228433 -0.01627604  0.54875505]]",
+        "desired_goal": "[[-1.6097871   1.0082495  -1.724538  ]\n [-0.0322743  -1.2531949  -0.39701077]\n [-0.34559652 -1.0852717  -0.07138722]\n [ 0.27549523 -0.5173385   1.0641928 ]]",
+        "observation": "[[ 0.39228433 -0.01627604  0.54875505  0.01306864 -0.00238104  0.00786286]\n [ 0.39228433 -0.01627604  0.54875505  0.01306864 -0.00238104  0.00786286]\n [ 0.39228433 -0.01627604  0.54875505  0.01306864 -0.00238104  0.00786286]\n [ 0.39228433 -0.01627604  0.54875505  0.01306864 -0.00238104  0.00786286]]"
+    },
+    "_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": "[[ 0.13406153 -0.05164449  0.10638399]\n [ 0.08254001  0.0615936   0.07961553]\n [ 0.09920212  0.124237    0.15782163]\n [-0.0037902  -0.04392442  0.18122642]]",
+        "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,
+    "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
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d7b88f6f37ed3d31f871cf36e2673b37a874cef5bf44e98ed83c6a7d48f00c7a
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d672c0e4a1874f33aaaea41701fa8e5761fd4af49102a26c301bd03d4439d0b2
+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: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- 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 0x7f3dcbbb01f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f3dcbbace40>"}, "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": 4, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1678896586649900642, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAftnIPlVVhbw2eww/ftnIPlVVhbw2eww/ftnIPlVVhbw2eww/ftnIPlVVhbw2eww/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAgQ3Ov1IOgT+pvdy/DzIEvbFooL//RMu+B/Kwvi/qir93M5K9tg2NPkxwBL94N4g/lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAAB+2cg+VVWFvDZ7DD/eHVY8TAscuzrTADx+2cg+VVWFvDZ7DD/eHVY8TAscuzrTADx+2cg+VVWFvDZ7DD/eHVY8TAscuzrTADx+2cg+VVWFvDZ7DD/eHVY8TAscuzrTADyUaA5LBEsGhpRoEnSUUpR1Lg==", "achieved_goal": "[[ 0.39228433 -0.01627604  0.54875505]\n [ 0.39228433 -0.01627604  0.54875505]\n [ 0.39228433 -0.01627604  0.54875505]\n [ 0.39228433 -0.01627604  0.54875505]]", "desired_goal": "[[-1.6097871   1.0082495  -1.724538  ]\n [-0.0322743  -1.2531949  -0.39701077]\n [-0.34559652 -1.0852717  -0.07138722]\n [ 0.27549523 -0.5173385   1.0641928 ]]", "observation": "[[ 0.39228433 -0.01627604  0.54875505  0.01306864 -0.00238104  0.00786286]\n [ 0.39228433 -0.01627604  0.54875505  0.01306864 -0.00238104  0.00786286]\n [ 0.39228433 -0.01627604  0.54875505  0.01306864 -0.00238104  0.00786286]\n [ 0.39228433 -0.01627604  0.54875505  0.01306864 -0.00238104  0.00786286]]"}, "_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": "[[ 0.13406153 -0.05164449  0.10638399]\n [ 0.08254001  0.0615936   0.07961553]\n [ 0.09920212  0.124237    0.15782163]\n [-0.0037902  -0.04392442  0.18122642]]", "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, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIV+nuOhuy/r+UhpRSlIwBbJRLMowBdJRHQKgLUUC7sfJ1fZQoaAZoCWgPQwiu1R72QiEEwJSGlFKUaBVLMmgWR0CoCwpOvdM1dX2UKGgGaAloD0MIg/jAjv+C97+UhpRSlGgVSzJoFkdAqAq5UPxx1nV9lChoBmgJaA9DCJ9XPPVIQwvAlIaUUpRoFUsyaBZHQKgKfPWxyGV1fZQoaAZoCWgPQwgPm8jMBe78v5SGlFKUaBVLMmgWR0CoDO5CF9KFdX2UKGgGaAloD0MI1H0AUpt49r+UhpRSlGgVSzJoFkdAqAynbsWweXV9lChoBmgJaA9DCCsU6X5OgQHAlIaUUpRoFUsyaBZHQKgMVgXuVop1fZQoaAZoCWgPQwiqfToeMxD8v5SGlFKUaBVLMmgWR0CoDBpSzgMudX2UKGgGaAloD0MILZW3I5xW/b+UhpRSlGgVSzJoFkdAqA6i9VWCE3V9lChoBmgJaA9DCD55WKg1Dfu/lIaUUpRoFUsyaBZHQKgOXHNorWl1fZQoaAZoCWgPQwiOyk3U0hz6v5SGlFKUaBVLMmgWR0CoDgtM495hdX2UKGgGaAloD0MIzPEKRE+KA8CUhpRSlGgVSzJoFkdAqA3PMEA5rHV9lChoBmgJaA9DCKhzRSkhGPa/lIaUUpRoFUsyaBZHQKgQR8wYced1fZQoaAZoCWgPQwjDuBtEawX3v5SGlFKUaBVLMmgWR0CoEAEnssxxdX2UKGgGaAloD0MI+zpwzoiSBsCUhpRSlGgVSzJoFkdAqA+vwqiGnHV9lChoBmgJaA9DCNE8gEV+rRLAlIaUUpRoFUsyaBZHQKgPc1WsA/91fZQoaAZoCWgPQwheTZ6ymu7/v5SGlFKUaBVLMmgWR0CoEeuHnEEUdX2UKGgGaAloD0MIR+nSvyTV9b+UhpRSlGgVSzJoFkdAqBGknCwbEXV9lChoBmgJaA9DCK/NxkrMs/m/lIaUUpRoFUsyaBZHQKgRU5wOvuB1fZQoaAZoCWgPQwiyDkdX6U4GwJSGlFKUaBVLMmgWR0CoERfYzzmPdX2UKGgGaAloD0MIEtkHWRbsCsCUhpRSlGgVSzJoFkdAqBNCgRK6F3V9lChoBmgJaA9DCDTY1HlUfAHAlIaUUpRoFUsyaBZHQKgS+vcJtzl1fZQoaAZoCWgPQwgr3PKRlLQMwJSGlFKUaBVLMmgWR0CoEqjzRQaadX2UKGgGaAloD0MIc9cS8kEPDsCUhpRSlGgVSzJoFkdAqBJsVJtix3V9lChoBmgJaA9DCJimCHB69wDAlIaUUpRoFUsyaBZHQKgUM/k/8l51fZQoaAZoCWgPQwhtkbQbfcwKwJSGlFKUaBVLMmgWR0CoE+x20Re1dX2UKGgGaAloD0MInIu/7QmS97+UhpRSlGgVSzJoFkdAqBOaVnmJWXV9lChoBmgJaA9DCK8/ic+dQAbAlIaUUpRoFUsyaBZHQKgTXW4EwFl1fZQoaAZoCWgPQwgW2jnNAq0JwJSGlFKUaBVLMmgWR0CoFTBL5AQhdX2UKGgGaAloD0MIKVyPwvWIAsCUhpRSlGgVSzJoFkdAqBTowblzVHV9lChoBmgJaA9DCG2MnfASHAzAlIaUUpRoFUsyaBZHQKgUlt1p0wJ1fZQoaAZoCWgPQwjn4m97gmQBwJSGlFKUaBVLMmgWR0CoFFohyKekdX2UKGgGaAloD0MI0sPQ6uQsBMCUhpRSlGgVSzJoFkdAqBY2TC+De3V9lChoBmgJaA9DCE5FKowtpAnAlIaUUpRoFUsyaBZHQKgV7wOOKfp1fZQoaAZoCWgPQwi8sgsG11wGwJSGlFKUaBVLMmgWR0CoFZ2qT8pDdX2UKGgGaAloD0MIBkoKLIDp+7+UhpRSlGgVSzJoFkdAqBVhPfsNUnV9lChoBmgJaA9DCFhYcD/gwQrAlIaUUpRoFUsyaBZHQKgXZS2H+Id1fZQoaAZoCWgPQwibN04K854BwJSGlFKUaBVLMmgWR0CoFx6cqe9SdX2UKGgGaAloD0MIwkzbv7KS+b+UhpRSlGgVSzJoFkdAqBbMlw97nnV9lChoBmgJaA9DCLKbGf1oWAXAlIaUUpRoFUsyaBZHQKgWj779AHF1fZQoaAZoCWgPQwgpmDEFa1wIwJSGlFKUaBVLMmgWR0CoGFNdzGPxdX2UKGgGaAloD0MImfViKCda97+UhpRSlGgVSzJoFkdAqBgLzErGznV9lChoBmgJaA9DCFrUJ7nDhgPAlIaUUpRoFUsyaBZHQKgXudupCKJ1fZQoaAZoCWgPQwhozvqUY9IRwJSGlFKUaBVLMmgWR0CoF3ztkWhzdX2UKGgGaAloD0MItMcL6fAQ+7+UhpRSlGgVSzJoFkdAqBk0rCm/FnV9lChoBmgJaA9DCLhYUYNpOAvAlIaUUpRoFUsyaBZHQKgY7PgNwzd1fZQoaAZoCWgPQwi+F1+0x+sIwJSGlFKUaBVLMmgWR0CoGJrQPZqVdX2UKGgGaAloD0MIH6LRHcSuCsCUhpRSlGgVSzJoFkdAqBhd34bjtHV9lChoBmgJaA9DCD3TS4xlegnAlIaUUpRoFUsyaBZHQKgaOSTQmeF1fZQoaAZoCWgPQwiamZmZmTkFwJSGlFKUaBVLMmgWR0CoGfGlqJuVdX2UKGgGaAloD0MIFVPpJ5w9EsCUhpRSlGgVSzJoFkdAqBmfk92X9nV9lChoBmgJaA9DCNU9srlq/gfAlIaUUpRoFUsyaBZHQKgZYxQizLR1fZQoaAZoCWgPQwh3hNOCF/35v5SGlFKUaBVLMmgWR0CoGy0aAFxGdX2UKGgGaAloD0MIeouH9xx4BMCUhpRSlGgVSzJoFkdAqBrlfw7T2HV9lChoBmgJaA9DCNWvdD48awPAlIaUUpRoFUsyaBZHQKgak3YL9dh1fZQoaAZoCWgPQwhuh4bFqNsQwJSGlFKUaBVLMmgWR0CoGlaV2Rq5dX2UKGgGaAloD0MIzoqoiT6/E8CUhpRSlGgVSzJoFkdAqBww7eVLSXV9lChoBmgJaA9DCNBFQ8ajVA7AlIaUUpRoFUsyaBZHQKgb6XHBDXx1fZQoaAZoCWgPQwizsRLzrAQEwJSGlFKUaBVLMmgWR0CoG5dph4MXdX2UKGgGaAloD0MIyf/k796xAcCUhpRSlGgVSzJoFkdAqBtbZ+QU6HV9lChoBmgJaA9DCKWfcHZrOQXAlIaUUpRoFUsyaBZHQKgdJCfpUxV1fZQoaAZoCWgPQwjsvfiiPR73v5SGlFKUaBVLMmgWR0CoHNyi/O+qdX2UKGgGaAloD0MIH/KWqx9bAsCUhpRSlGgVSzJoFkdAqByK6DoQnXV9lChoBmgJaA9DCCxJnuv70AbAlIaUUpRoFUsyaBZHQKgcTjvNNah1fZQoaAZoCWgPQwgS9YJPc7IEwJSGlFKUaBVLMmgWR0CoHhBInSfEdX2UKGgGaAloD0MI8fEJ2Xl7DcCUhpRSlGgVSzJoFkdAqB3JA4XGfnV9lChoBmgJaA9DCKPlQA+1DQfAlIaUUpRoFUsyaBZHQKgddvgm7at1fZQoaAZoCWgPQwh0QuigS1gGwJSGlFKUaBVLMmgWR0CoHToYekpJdX2UKGgGaAloD0MIOsssQrHlEMCUhpRSlGgVSzJoFkdAqB8Ya1kUbnV9lChoBmgJaA9DCEiKyLCKVwTAlIaUUpRoFUsyaBZHQKge0PBBRht1fZQoaAZoCWgPQwgZ/z7jwqEEwJSGlFKUaBVLMmgWR0CoHn7qIJqqdX2UKGgGaAloD0MIGTp2UInr/b+UhpRSlGgVSzJoFkdAqB5CFXaJynV9lChoBmgJaA9DCLlxi/m5QQHAlIaUUpRoFUsyaBZHQKggOXxe9jB1fZQoaAZoCWgPQwjvGvSlt78PwJSGlFKUaBVLMmgWR0CoH/IJRfnfdX2UKGgGaAloD0MIbAn5oGdTCcCUhpRSlGgVSzJoFkdAqB+f6TGHYnV9lChoBmgJaA9DCPKU1XQ9EQXAlIaUUpRoFUsyaBZHQKgfY0Xxe9l1fZQoaAZoCWgPQwiKWMSww7gOwJSGlFKUaBVLMmgWR0CoISOtfXwtdX2UKGgGaAloD0MI++b+6nG/CcCUhpRSlGgVSzJoFkdAqCDcVafSQnV9lChoBmgJaA9DCLe4xmeynxLAlIaUUpRoFUsyaBZHQKggiigTRIB1fZQoaAZoCWgPQwhWuyakNYYTwJSGlFKUaBVLMmgWR0CoIE1tGd7OdX2UKGgGaAloD0MIUB2rlJ5p97+UhpRSlGgVSzJoFkdAqCIXRb8m8nV9lChoBmgJaA9DCAiQoWMHVQHAlIaUUpRoFUsyaBZHQKghz6Ww/xF1fZQoaAZoCWgPQwhrKLUX0ZYOwJSGlFKUaBVLMmgWR0CoIX16u4gBdX2UKGgGaAloD0MIRDS6g9i5AcCUhpRSlGgVSzJoFkdAqCFAlv60pnV9lChoBmgJaA9DCH6nyYy3lQzAlIaUUpRoFUsyaBZHQKgjGljVhCt1fZQoaAZoCWgPQwgra5vicREBwJSGlFKUaBVLMmgWR0CoItLNnoPkdX2UKGgGaAloD0MIkDF3LSHvEMCUhpRSlGgVSzJoFkdAqCKAzk6tDHV9lChoBmgJaA9DCNfAVgkWhw7AlIaUUpRoFUsyaBZHQKgiRC0F8oh1fZQoaAZoCWgPQwhw7xr0pff8v5SGlFKUaBVLMmgWR0CoJBD8LroodX2UKGgGaAloD0MInKVkOQnFAMCUhpRSlGgVSzJoFkdAqCPJV6u4gHV9lChoBmgJaA9DCJo/prVp7APAlIaUUpRoFUsyaBZHQKgjdyLAHml1fZQoaAZoCWgPQwijIHh8e1f/v5SGlFKUaBVLMmgWR0CoIzpKaodddX2UKGgGaAloD0MIkPmAQGeSC8CUhpRSlGgVSzJoFkdAqCUFGTcIq3V9lChoBmgJaA9DCM6MfjSc8v2/lIaUUpRoFUsyaBZHQKgkvb0OEuh1fZQoaAZoCWgPQwhCzCVV260PwJSGlFKUaBVLMmgWR0CoJGv1+RYBdX2UKGgGaAloD0MI9pUH6SlyDcCUhpRSlGgVSzJoFkdAqCQvNX5nDnV9lChoBmgJaA9DCDkroib6PP+/lIaUUpRoFUsyaBZHQKgl6stCiRJ1fZQoaAZoCWgPQwiI1/ULduMCwJSGlFKUaBVLMmgWR0CoJaMa0hNedX2UKGgGaAloD0MIOgMjL2sSGMCUhpRSlGgVSzJoFkdAqCVQ5/9YOnV9lChoBmgJaA9DCPA0mfG28gjAlIaUUpRoFUsyaBZHQKglE/RE4Nt1ZS4="}, "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, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -3.2821256149560214, "std_reward": 1.2482106844246446, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-15T17:08:28.636217"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c16acadc236e44340f6a7f8ccb1888dd959917dedd797bd91bc4794df06859e7
+size 3056