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: -8.19 +/- 4.24
+      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:a78f71a4c97cb3f6d8d370d93a2aeddfa497911302e78d014f2139b9c8471e07
+size 108016

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 0x7f8e2a01d550>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f8e2a01e5c0>"
+    },
+    "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": 1679680078606625737,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "gAWVuwEAAAAAAACMC2NvbGxlY3Rpb25zlIwLT3JkZXJlZERpY3SUk5QpUpQojA1hY2hpZXZlZF9nb2FslIwSbnVtcHkuY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QoljAAAAAAAAAAZErdPjeI0ztH4BM/ZErdPjeI0ztH4BM/ZErdPjeI0ztH4BM/ZErdPjeI0ztH4BM/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwOGlIwBQ5R0lFKUjAxkZXNpcmVkX2dvYWyUaAcoljAAAAAAAAAAeG3vPuU7hr6VF6W/DAKOP6cdC78VnrE/ZtK9v5gkRDwHy9Y+K7/zPj8IZj9PBtQ/lGgOSwRLA4aUaBJ0lFKUjAtvYnNlcnZhdGlvbpRoByiWYAAAAAAAAABkSt0+N4jTO0fgEz/35mA7BakdOiUWhLxkSt0+N4jTO0fgEz/35mA7BakdOiUWhLxkSt0+N4jTO0fgEz/35mA7BakdOiUWhLxkSt0+N4jTO0fgEz/35mA7BakdOiUWhLyUaA5LBEsGhpRoEnSUUpR1Lg==",
+        "achieved_goal": "[[0.43220818 0.00645545 0.57764095]\n [0.43220818 0.00645545 0.57764095]\n [0.43220818 0.00645545 0.57764095]\n [0.43220818 0.00645545 0.57764095]]",
+        "desired_goal": "[[ 0.46763206 -0.2621757  -1.2897822 ]\n [ 1.1094375  -0.5434212   1.3876368 ]\n [-1.4829834   0.01197162  0.41951773]\n [ 0.47606787  0.8985633   1.6564425 ]]",
+        "observation": "[[ 0.43220818  0.00645545  0.57764095  0.00343174  0.00060143 -0.01612384]\n [ 0.43220818  0.00645545  0.57764095  0.00343174  0.00060143 -0.01612384]\n [ 0.43220818  0.00645545  0.57764095  0.00343174  0.00060143 -0.01612384]\n [ 0.43220818  0.00645545  0.57764095  0.00343174  0.00060143 -0.01612384]]"
+    },
+    "_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": "[[-0.13775492 -0.06131103  0.11113501]\n [-0.10957328  0.07582432  0.11304065]\n [ 0.11386839 -0.01762167  0.14143309]\n [-0.11846655  0.10256513  0.05770473]]",
+        "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 sha256:4b980f7654b434a0e393c346b2477d527927cf62b6f5065eba710162a737f280
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ccd70fa56e63c3cee738a7dc2b03b45838476fb02738bd850c69b2dccab63d28
+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 0x7f8e2a01d550>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f8e2a01e5c0>"}, "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": 1679680078606625737, "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": "[[0.43220818 0.00645545 0.57764095]\n [0.43220818 0.00645545 0.57764095]\n [0.43220818 0.00645545 0.57764095]\n [0.43220818 0.00645545 0.57764095]]", "desired_goal": "[[ 0.46763206 -0.2621757  -1.2897822 ]\n [ 1.1094375  -0.5434212   1.3876368 ]\n [-1.4829834   0.01197162  0.41951773]\n [ 0.47606787  0.8985633   1.6564425 ]]", "observation": "[[ 0.43220818  0.00645545  0.57764095  0.00343174  0.00060143 -0.01612384]\n [ 0.43220818  0.00645545  0.57764095  0.00343174  0.00060143 -0.01612384]\n [ 0.43220818  0.00645545  0.57764095  0.00343174  0.00060143 -0.01612384]\n [ 0.43220818  0.00645545  0.57764095  0.00343174  0.00060143 -0.01612384]]"}, "_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": "[[-0.13775492 -0.06131103  0.11113501]\n [-0.10957328  0.07582432  0.11304065]\n [ 0.11386839 -0.01762167  0.14143309]\n [-0.11846655  0.10256513  0.05770473]]", "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////9LAHSUYkMIZRpNLsbwIsCUhpRSlIwBbJRLMowBdJRHQKZ5JX4j8k51fZQoaAZoCWgPQwjjb3uCxPYgwJSGlFKUaBVLMmgWR0CmeNGqxTsIdX2UKGgGaAloD0MIiEhNu5gWGsCUhpRSlGgVSzJoFkdApnh7bpNbknV9lChoBmgJaA9DCEhS0sPQygTAlIaUUpRoFUsyaBZHQKZ4KMxXXAd1fZQoaAZoCWgPQwiwdD48S7ATwJSGlFKUaBVLMmgWR0CmeuHAymALdX2UKGgGaAloD0MI9inHZHGfJsCUhpRSlGgVSzJoFkdApnqPbXYlIHV9lChoBmgJaA9DCB/ZXDXPQRXAlIaUUpRoFUsyaBZHQKZ6OTt9hJB1fZQoaAZoCWgPQwj+YrZkVdQXwJSGlFKUaBVLMmgWR0Cmeeh7eEZjdX2UKGgGaAloD0MIB++rcqEyC8CUhpRSlGgVSzJoFkdApnzMc4o7WHV9lChoBmgJaA9DCA2NJ4I4fy3AlIaUUpRoFUsyaBZHQKZ8eOOKfnR1fZQoaAZoCWgPQwgEOShhpl0WwJSGlFKUaBVLMmgWR0CmfCLG7z06dX2UKGgGaAloD0MIRDAOLh0bIsCUhpRSlGgVSzJoFkdApnvQ4EOiFnV9lChoBmgJaA9DCMHhBRGpyRDAlIaUUpRoFUsyaBZHQKZ95fJmukl1fZQoaAZoCWgPQwhT6/1GO+YgwJSGlFKUaBVLMmgWR0CmfZEn1FpgdX2UKGgGaAloD0MIwcdgxak2EMCUhpRSlGgVSzJoFkdApn06WRigCnV9lChoBmgJaA9DCDBHj9/b7CLAlIaUUpRoFUsyaBZHQKZ850dzXBh1fZQoaAZoCWgPQwhGRZxOso0hwJSGlFKUaBVLMmgWR0CmfuRjBl+WdX2UKGgGaAloD0MIdCUC1T9IEMCUhpRSlGgVSzJoFkdApn6PqJMxoXV9lChoBmgJaA9DCE+Q2O4esB/AlIaUUpRoFUsyaBZHQKZ+OLm6oVF1fZQoaAZoCWgPQwiEns2qz1UIwJSGlFKUaBVLMmgWR0CmfeViWmgrdX2UKGgGaAloD0MIXRlUG5wAKcCUhpRSlGgVSzJoFkdApn/uMwUQCnV9lChoBmgJaA9DCKURM/s87iXAlIaUUpRoFUsyaBZHQKZ/mZAIIGB1fZQoaAZoCWgPQwh0C12JQPURwJSGlFKUaBVLMmgWR0Cmf0K8UVSGdX2UKGgGaAloD0MI3UJXIlB9AcCUhpRSlGgVSzJoFkdApn7vZbpu/HV9lChoBmgJaA9DCAx2w7ZFCRDAlIaUUpRoFUsyaBZHQKaA35HEuQJ1fZQoaAZoCWgPQwjGTQ00n7shwJSGlFKUaBVLMmgWR0CmgIrSuyNXdX2UKGgGaAloD0MIrmcIxyw7J8CUhpRSlGgVSzJoFkdApoA0W43FUHV9lChoBmgJaA9DCNbllICY1BHAlIaUUpRoFUsyaBZHQKZ/4Q8wHqx1fZQoaAZoCWgPQwhiMepae88QwJSGlFKUaBVLMmgWR0CmgcbojfNzdX2UKGgGaAloD0MIKEnXTL45JMCUhpRSlGgVSzJoFkdApoFyIxgy/XV9lChoBmgJaA9DCGixFMlXkhjAlIaUUpRoFUsyaBZHQKaBG1WKdhB1fZQoaAZoCWgPQwgm/FI/b6oPwJSGlFKUaBVLMmgWR0CmgMhHCoCNdX2UKGgGaAloD0MIBP7w89+DGsCUhpRSlGgVSzJoFkdApoK25paibnV9lChoBmgJaA9DCCtoWmJlLCPAlIaUUpRoFUsyaBZHQKaCYmReTmp1fZQoaAZoCWgPQwi77xge++EjwJSGlFKUaBVLMmgWR0Cmgguvt+kQdX2UKGgGaAloD0MIpaKx9nfWGcCUhpRSlGgVSzJoFkdApoG4ZEUj9nV9lChoBmgJaA9DCBsv3SQGIQTAlIaUUpRoFUsyaBZHQKaDqg5BC2N1fZQoaAZoCWgPQwh8fa1LjSAywJSGlFKUaBVLMmgWR0Cmg1W3Sa3JdX2UKGgGaAloD0MI9KRMamjTGsCUhpRSlGgVSzJoFkdApoL+9WZJCnV9lChoBmgJaA9DCD6uDRXjHBfAlIaUUpRoFUsyaBZHQKaCq9AX2uh1fZQoaAZoCWgPQwgR4PQu3k8ZwJSGlFKUaBVLMmgWR0CmhOF1B+nZdX2UKGgGaAloD0MIWBtjJ7xkJsCUhpRSlGgVSzJoFkdApoSNUyYXwnV9lChoBmgJaA9DCN9wH7k1ESbAlIaUUpRoFUsyaBZHQKaEOG1x82J1fZQoaAZoCWgPQwhJu9HHfNAbwJSGlFKUaBVLMmgWR0Cmg+Uq6OHWdX2UKGgGaAloD0MIUb8LW7O1IcCUhpRSlGgVSzJoFkdApoXTPOY6XHV9lChoBmgJaA9DCCGRtvEnuhnAlIaUUpRoFUsyaBZHQKaFfrOZ9eB1fZQoaAZoCWgPQwg1JsRcUlUhwJSGlFKUaBVLMmgWR0CmhSgCnxaxdX2UKGgGaAloD0MIPWU1XU/UC8CUhpRSlGgVSzJoFkdApoTUu3+db3V9lChoBmgJaA9DCNPddTbk/xHAlIaUUpRoFUsyaBZHQKaG1PtUn5V1fZQoaAZoCWgPQwhtAgzLn08gwJSGlFKUaBVLMmgWR0CmhoDafzz3dX2UKGgGaAloD0MI6BGj5xYaEMCUhpRSlGgVSzJoFkdApoYqo4uK43V9lChoBmgJaA9DCG1y+KQTuR7AlIaUUpRoFUsyaBZHQKaF12ZiNKh1fZQoaAZoCWgPQwiSek/ltMcPwJSGlFKUaBVLMmgWR0Cmh8Z3kgfVdX2UKGgGaAloD0MIy0dS0sOAGsCUhpRSlGgVSzJoFkdApodxvvSc9XV9lChoBmgJaA9DCD874Lpiph3AlIaUUpRoFUsyaBZHQKaHG0pmVZ91fZQoaAZoCWgPQwiA12fO+qQTwJSGlFKUaBVLMmgWR0CmhsfpUxVRdX2UKGgGaAloD0MIChFwCFVKGcCUhpRSlGgVSzJoFkdApoitCPZIx3V9lChoBmgJaA9DCGmOrPwySBXAlIaUUpRoFUsyaBZHQKaIWGoJiRZ1fZQoaAZoCWgPQwg74pANpLsQwJSGlFKUaBVLMmgWR0CmiAGV7hNudX2UKGgGaAloD0MIHvzEAfT7GcCUhpRSlGgVSzJoFkdApoeuOsDGLnV9lChoBmgJaA9DCDNt/8pKgxPAlIaUUpRoFUsyaBZHQKaJtw+dK/V1fZQoaAZoCWgPQwh9yjFZ3H8hwJSGlFKUaBVLMmgWR0CmiWJztCzDdX2UKGgGaAloD0MIyeiAJOxLHMCUhpRSlGgVSzJoFkdApokMmjTKDHV9lChoBmgJaA9DCKlqgqj7UBrAlIaUUpRoFUsyaBZHQKaIuVQAMlV1fZQoaAZoCWgPQwhtAaH18IUVwJSGlFKUaBVLMmgWR0CmiqNYr8R+dX2UKGgGaAloD0MIjZYDPdRWE8CUhpRSlGgVSzJoFkdApopOlGgBcXV9lChoBmgJaA9DCDQvh913XBLAlIaUUpRoFUsyaBZHQKaJ97XQMQV1fZQoaAZoCWgPQwh7iEZ3EPsiwJSGlFKUaBVLMmgWR0CmiaRwQ176dX2UKGgGaAloD0MIfo6PFmdUI8CUhpRSlGgVSzJoFkdApoupaV2RrHV9lChoBmgJaA9DCF/uk6MAsRfAlIaUUpRoFUsyaBZHQKaLVNO/L1V1fZQoaAZoCWgPQwi62or9Ze8gwJSGlFKUaBVLMmgWR0Cmiv4TsY2sdX2UKGgGaAloD0MIoijQJ/KEEcCUhpRSlGgVSzJoFkdApoqrUgB91HV9lChoBmgJaA9DCD5bBwd7AyrAlIaUUpRoFUsyaBZHQKaMpXPqs2h1fZQoaAZoCWgPQwhfzmxX6AMOwJSGlFKUaBVLMmgWR0CmjFDiXIEKdX2UKGgGaAloD0MIowVoW826FcCUhpRSlGgVSzJoFkdApov6cEvCdnV9lChoBmgJaA9DCDxM++b+6hjAlIaUUpRoFUsyaBZHQKaLpxMnJDF1fZQoaAZoCWgPQwjy0k1iEPgpwJSGlFKUaBVLMmgWR0CmjaZOSGJvdX2UKGgGaAloD0MIhT/DmzWIFcCUhpRSlGgVSzJoFkdApo1Rq/M4cXV9lChoBmgJaA9DCAYtJGB0mRDAlIaUUpRoFUsyaBZHQKaM+tGNJe51fZQoaAZoCWgPQwi5+rFJfvA6wJSGlFKUaBVLMmgWR0CmjKfIsAeadX2UKGgGaAloD0MI8db5t8tKMMCUhpRSlGgVSzJoFkdApo7CYzBRAXV9lChoBmgJaA9DCIFZoUj36yTAlIaUUpRoFUsyaBZHQKaObZ+QU6B1fZQoaAZoCWgPQwiRXz/EBnstwJSGlFKUaBVLMmgWR0Cmjhg0TDfndX2UKGgGaAloD0MIXBsqxvk7EsCUhpRSlGgVSzJoFkdApo3FA5aNdnV9lChoBmgJaA9DCEBqEyf3uwnAlIaUUpRoFUsyaBZHQKaPv2HLzPN1fZQoaAZoCWgPQwhZvi7Df2I2wJSGlFKUaBVLMmgWR0Cmj2sOf/WEdX2UKGgGaAloD0MIryZPWU0/KsCUhpRSlGgVSzJoFkdApo8UbcXWOXV9lChoBmgJaA9DCDhLyXISehjAlIaUUpRoFUsyaBZHQKaOwVafSQZ1fZQoaAZoCWgPQwjhtOBFX6kvwJSGlFKUaBVLMmgWR0CmkMDQAuIzdX2UKGgGaAloD0MItd5vtOPmCsCUhpRSlGgVSzJoFkdAppBsHjZL7HV9lChoBmgJaA9DCCKLNPEOVDLAlIaUUpRoFUsyaBZHQKaQFbAUL2J1fZQoaAZoCWgPQwgsnQ/PEuQXwJSGlFKUaBVLMmgWR0Cmj8J66asqdX2UKGgGaAloD0MIKqp+pfOxLsCUhpRSlGgVSzJoFkdAppHyraM72nV9lChoBmgJaA9DCBRcrKjB1BXAlIaUUpRoFUsyaBZHQKaRnqptJnR1fZQoaAZoCWgPQwjyzqEMVZEbwJSGlFKUaBVLMmgWR0CmkUhn8KoidX2UKGgGaAloD0MIaFw4EJL1HMCUhpRSlGgVSzJoFkdAppD1/rjYI3V9lChoBmgJaA9DCI8dVOI6fiXAlIaUUpRoFUsyaBZHQKaTX7u2JBR1fZQoaAZoCWgPQwgceouH9zwOwJSGlFKUaBVLMmgWR0CmkwtzCDVZdX2UKGgGaAloD0MIdAmH3uIxIMCUhpRSlGgVSzJoFkdAppK1A7gbZXV9lChoBmgJaA9DCD9xAP2+rx7AlIaUUpRoFUsyaBZHQKaSYiwjdHl1ZS4="}, "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": -8.18675722265616, "std_reward": 4.241031288068725, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-24T18:36:12.255480"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7fb26318223db2bf16b9224cfd226e96ba81217a68b5ea806ba05d0df06eb475
+size 3056