Initial commit

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- AntBulletEnv-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: AntBulletEnv-v0
+      type: AntBulletEnv-v0
+    metrics:
+    - type: mean_reward
+      value: 1481.60 +/- 117.72
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **AntBulletEnv-v0**
+This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
+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-AntBulletEnv-v0.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:18d83dd15f292af7e46790fa52862f2f5dc5da5239627bfbaca2442b35ee7206
+size 129065

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.2

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,105 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    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\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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\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 ActorCriticPolicy.__init__ at 0x7fcf69e2b040>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcf69e2b0d0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcf69e2b160>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcf69e2b1f0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fcf69e2b280>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fcf69e2b310>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcf69e2b3a0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fcf69e2b430>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcf69e2b4c0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcf69e2b550>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcf69e2b5e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fcf69ea74b0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu",
+        "log_std_init": -2,
+        "ortho_init": false,
+        "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
+        "optimizer_kwargs": {
+            "alpha": 0.99,
+            "eps": 1e-05,
+            "weight_decay": 0
+        }
+    },
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            28
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]",
+        "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]",
+        "high": "[1. 1. 1. 1. 1. 1. 1. 1.]",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "num_timesteps": 2000000,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1670612489225491971,
+    "learning_rate": 0.00096,
+    "tensorboard_log": "./tensorboard",
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_episode_num": 0,
+    "use_sde": true,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.0,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVRAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQH/Lu3lS0jWMAWyUTegDjAF0lEdAp2nWAI6bOXV9lChoBkdAha28cMmWt2gHTegDaAhHQKdrIq1gH/t1fZQoaAZHQI3maRSxZ+xoB03oA2gIR0CncbBxYJVsdX2UKGgGR0CWEdus90RwaAdN6ANoCEdAp3W6rYGt63V9lChoBkdAclN/bj94vGgHTegDaAhHQKd2HXtBv751fZQoaAZHQISs+knCwbFoB03oA2gIR0Cnd3fdAPd3dX2UKGgGR0CWuCr8BMi9aAdN6ANoCEdAp4F1OKwY+HV9lChoBkdAlT1KGDcuamgHTegDaAhHQKeFfnA6+391fZQoaAZHQJOu7EKmbb1oB03oA2gIR0Cnhd2r4nF6dX2UKGgGR0CYJSxMnJDFaAdN6ANoCEdAp4cwkmhM8HV9lChoBkdAkzQ4E4ecQWgHTegDaAhHQKeN47g88tB1fZQoaAZHQJNDbb+Lm6poB03oA2gIR0Cnke0kv9LpdX2UKGgGR0CRYwrbQC0XaAdN6ANoCEdAp5JHywwCbXV9lChoBkdAl0t+YD1XeWgHTegDaAhHQKeTlEtNBWx1fZQoaAZHQJVCnSkTHsFoB03oA2gIR0CnmjteMQ2/dX2UKGgGR0CT3Jbuc+aCaAdN6ANoCEdAp55TNSqEOHV9lChoBkdAlNkbxAjY7WgHTegDaAhHQKeesH2RJVd1fZQoaAZHQJQVlcTrVvxoB03oA2gIR0Cnn/wlruYydX2UKGgGR0CWHh6BRQ7+aAdN6ANoCEdAp6adIK+i8HV9lChoBkdAk9bVpfx+a2gHTegDaAhHQKeqor3j+711fZQoaAZHQGy2sLF4s3BoB03oA2gIR0Cnqwki2UjcdX2UKGgGR0CUCB2Jzkp7aAdN6ANoCEdAp6xhqIrOJXV9lChoBkdAkPExxPwd82gHTegDaAhHQKezBQdjoZB1fZQoaAZHQGoXFKkEcKhoB03oA2gIR0Cntxd/8VHndX2UKGgGR0CVqA+ZgG8maAdN6ANoCEdAp7d2PYFqz3V9lChoBkdAcXGogmqo62gHTegDaAhHQKe4zJW/8EV1fZQoaAZHQGcPJGOMl1NoB03oA2gIR0Cnv3oSL61tdX2UKGgGR0BkUP2TPjXGaAdN6ANoCEdAp8OItWdVenV9lChoBkdAZpJfzBhx52gHTegDaAhHQKfD5/jsD4h1fZQoaAZHQJgHCuQp4KRoB03oA2gIR0CnxTnrQgLadX2UKGgGR0CVvFcGC7K8aAdN6ANoCEdAp8vrSqlxfnV9lChoBkdAkE7CT+vQnmgHTegDaAhHQKfP4nAIpph1fZQoaAZHQJckR1xKg7JoB03oA2gIR0Cn0D1vES/TdX2UKGgGR0CU7njL0SRKaAdN6ANoCEdAp9GMRlHz6XV9lChoBkdAl/xhf8dgfGgHTegDaAhHQKfYGhib2Dh1fZQoaAZHQJWwu/ag261oB03oA2gIR0Cn3B74Ju2rdX2UKGgGR0CKzlPM0P6LaAdN6ANoCEdAp9yEFSsKcHV9lChoBkdAkpvsbzbvgGgHTegDaAhHQKfd3y5I6Kd1fZQoaAZHwD/+DQJHAh1oB03oA2gIR0Cn5JE1/DtPdX2UKGgGR0B6+v7rLQokaAdN6ANoCEdAp+ikY4yXU3V9lChoBkdAb5Z/8VHnU2gHTegDaAhHQKfpAu7pV0d1fZQoaAZHQAKx28qWkadoB03oA2gIR0Cn6lH9vS+hdX2UKGgGR0CF04zTnaFmaAdN6ANoCEdAp/DvvnbItHV9lChoBkdAlRBMzQ/oq2gHTegDaAhHQKf1AyhzvJB1fZQoaAZHQJil3HEMspZoB03oA2gIR0Cn9WUTcqOMdX2UKGgGR0CUO7Gkep4saAdN6ANoCEdAp/atitq59XV9lChoBkdAlyo0pqh11WgHTegDaAhHQKf9P2saKk51fZQoaAZHQFtvV1Oj7ANoB03oA2gIR0CoAVMLF4s3dX2UKGgGR0CS1lJZW7voaAdN6ANoCEdAqAG3bVSXMXV9lChoBkdAkRBkQkHD8GgHTegDaAhHQKgDDrAP/aR1fZQoaAZHQJOAH0163RZoB03oA2gIR0CoCdfLcKw7dX2UKGgGR0ByBTCtRvWIaAdN6ANoCEdAqA3W2VmjCnV9lChoBkdAl1ZmNJe3QWgHTegDaAhHQKgOO5HVf/p1fZQoaAZHQJLD6oS+QEJoB03oA2gIR0CoD4S1Vo6CdX2UKGgGR0CR98I9kjHGaAdN6ANoCEdAqBYY/NZ/1HV9lChoBkdAkMKgC8vmHWgHTegDaAhHQKgaNgmZ3LV1fZQoaAZHQJLc39KmKqJoB03oA2gIR0CoGpMmv4dqdX2UKGgGR0CH/8bVjI7vaAdN6ANoCEdAqBvm4gA6uHV9lChoBkdAj+ju5jH4oWgHTegDaAhHQKgihMJx//h1fZQoaAZHQJHFffbblBBoB03oA2gIR0CoJnlm4AjqdX2UKGgGR0CRARg9Net0aAdN6ANoCEdAqCbZ20Re1XV9lChoBkdAX4olyBClamgHTegDaAhHQKgoILAHmih1fZQoaAZHQJINJaC+UQloB03oA2gIR0CoLqx7zCk5dX2UKGgGR0CNaW/nnuAqaAdN6ANoCEdAqDLb19ORDHV9lChoBkdAkfFt4VymymgHTegDaAhHQKgzOquKXOZ1fZQoaAZHQJNa7PdEb5xoB03oA2gIR0CoNRqwQlKLdX2UKGgGR0CRfXEfDDTCaAdN6ANoCEdAqDzlpyp71XV9lChoBkdAk/wKQNkOJGgHTegDaAhHQKhA6x46fap1fZQoaAZHQJFxqa2F36hoB03oA2gIR0CoQU/GEPDpdX2UKGgGR0CTach24d6taAdN6ANoCEdAqEKfrIHTqnV9lChoBkdAldPBnFo+OmgHTegDaAhHQKhJH9Sde6Z1fZQoaAZHQJEINVOsT39oB03oA2gIR0CoTSvc8DB/dX2UKGgGR0CXMG8ujASGaAdN6ANoCEdAqE2Pw3HaOHV9lChoBkdAk+uDewcHW2gHTegDaAhHQKhO2IyCWeJ1fZQoaAZHQJAwhXfZVXFoB03oA2gIR0CoVYAksz2wdX2UKGgGR0CL1QfT1CgLaAdN6ANoCEdAqFmYcDKYA3V9lChoBkdAlH9/tY0VJ2gHTegDaAhHQKhZ9n9Nvfl1fZQoaAZHQJJarVd5Y5loB03oA2gIR0CoW0Ml1KXfdX2UKGgGR0CSdjLRa5f/aAdN6ANoCEdAqGHUurZJ1HV9lChoBkdAkBA8xfv4NGgHTegDaAhHQKhl67EpAlh1fZQoaAZHQJPKebBoEjhoB03oA2gIR0CoZk9B8hLXdX2UKGgGR0CUQViX6ZYxaAdN6ANoCEdAqGeqySmqHXV9lChoBkdAkQLDujRD1GgHTegDaAhHQKhuIoybhFV1fZQoaAZHQJFFUMtsen1oB03oA2gIR0CociMZpBX0dX2UKGgGR0CFPMyY5T60aAdN6ANoCEdAqHKIaHbh33V9lChoBkdAkYVkI5YHPmgHTegDaAhHQKhz5Jmukk91fZQoaAZHQJKvRzzVc2RoB03oA2gIR0Coeom1pj+adX2UKGgGR0CTQbN21UlzaAdN6ANoCEdAqH7Ai9qUNnV9lChoBkdAk6rQ7o0Q9WgHTegDaAhHQKh/Hm4Ajpt1fZQoaAZHQJQQPRArxy5oB03oA2gIR0CogHKjrRjSdX2UKGgGR0CJxiqR2bG4aAdN6ANoCEdAqIcNfmcOLHV9lChoBkdAk2QNhZyMk2gHTegDaAhHQKiLErPt2LZ1fZQoaAZHQJJZu5tm+TNoB03oA2gIR0Coi24gRsdldX2UKGgGR0CN9uqjJuEVaAdN6ANoCEdAqIy9M495hXV9lChoBkdAcvvID5j6N2gHTegDaAhHQKiTbZf2K2t1fZQoaAZHQIf0SdFvybxoB03oA2gIR0Col3VNYbKidX2UKGgGR0B0ZmkbgjyGaAdN6ANoCEdAqJfVXLeQ+3V9lChoBkdAgog+bVjI72gHTegDaAhHQKiZI5hjOLR1fZQoaAZHQJRPSWZ7XxxoB03oA2gIR0Con8ircTJydX2UKGgGR0CTi+N9H+ZPaAdN6ANoCEdAqKPGJSBK+XVlLg=="
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 62500,
+    "n_steps": 8,
+    "gamma": 0.99,
+    "gae_lambda": 0.9,
+    "ent_coef": 0.0,
+    "vf_coef": 0.4,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": false
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0253a7aca406c9f4a6080aa81d06557f6f351a92c89c1d26f3fa3f620180f160
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1885338a7fcd1a8daa2b600af7ae978e5fe7435304331368b6b51c54a93d7ecf
+size 56766

a2c-AntBulletEnv-v0/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

a2c-AntBulletEnv-v0/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.8.16
+Stable-Baselines3: 1.6.2
+PyTorch: 1.13.0+cu116
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    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\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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\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 ActorCriticPolicy.__init__ at 0x7fcf69e2b040>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcf69e2b0d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcf69e2b160>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcf69e2b1f0>", "_build": "<function ActorCriticPolicy._build at 0x7fcf69e2b280>", "forward": "<function ActorCriticPolicy.forward at 0x7fcf69e2b310>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcf69e2b3a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fcf69e2b430>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcf69e2b4c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcf69e2b550>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcf69e2b5e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fcf69ea74b0>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [28], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf\n inf inf inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "bounded_above": "[False False False False False False False False False False False False\n False False False False False False False False False False False False\n False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-1. -1. -1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1. 1. 1.]", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_np_random": null}, "n_envs": 4, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1670612489225491971, "learning_rate": 0.00096, "tensorboard_log": "./tensorboard", "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_episode_num": 0, "use_sde": true, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVRAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQH/Lu3lS0jWMAWyUTegDjAF0lEdAp2nWAI6bOXV9lChoBkdAha28cMmWt2gHTegDaAhHQKdrIq1gH/t1fZQoaAZHQI3maRSxZ+xoB03oA2gIR0CncbBxYJVsdX2UKGgGR0CWEdus90RwaAdN6ANoCEdAp3W6rYGt63V9lChoBkdAclN/bj94vGgHTegDaAhHQKd2HXtBv751fZQoaAZHQISs+knCwbFoB03oA2gIR0Cnd3fdAPd3dX2UKGgGR0CWuCr8BMi9aAdN6ANoCEdAp4F1OKwY+HV9lChoBkdAlT1KGDcuamgHTegDaAhHQKeFfnA6+391fZQoaAZHQJOu7EKmbb1oB03oA2gIR0Cnhd2r4nF6dX2UKGgGR0CYJSxMnJDFaAdN6ANoCEdAp4cwkmhM8HV9lChoBkdAkzQ4E4ecQWgHTegDaAhHQKeN47g88tB1fZQoaAZHQJNDbb+Lm6poB03oA2gIR0Cnke0kv9LpdX2UKGgGR0CRYwrbQC0XaAdN6ANoCEdAp5JHywwCbXV9lChoBkdAl0t+YD1XeWgHTegDaAhHQKeTlEtNBWx1fZQoaAZHQJVCnSkTHsFoB03oA2gIR0CnmjteMQ2/dX2UKGgGR0CT3Jbuc+aCaAdN6ANoCEdAp55TNSqEOHV9lChoBkdAlNkbxAjY7WgHTegDaAhHQKeesH2RJVd1fZQoaAZHQJQVlcTrVvxoB03oA2gIR0Cnn/wlruYydX2UKGgGR0CWHh6BRQ7+aAdN6ANoCEdAp6adIK+i8HV9lChoBkdAk9bVpfx+a2gHTegDaAhHQKeqor3j+711fZQoaAZHQGy2sLF4s3BoB03oA2gIR0Cnqwki2UjcdX2UKGgGR0CUCB2Jzkp7aAdN6ANoCEdAp6xhqIrOJXV9lChoBkdAkPExxPwd82gHTegDaAhHQKezBQdjoZB1fZQoaAZHQGoXFKkEcKhoB03oA2gIR0Cntxd/8VHndX2UKGgGR0CVqA+ZgG8maAdN6ANoCEdAp7d2PYFqz3V9lChoBkdAcXGogmqo62gHTegDaAhHQKe4zJW/8EV1fZQoaAZHQGcPJGOMl1NoB03oA2gIR0Cnv3oSL61tdX2UKGgGR0BkUP2TPjXGaAdN6ANoCEdAp8OItWdVenV9lChoBkdAZpJfzBhx52gHTegDaAhHQKfD5/jsD4h1fZQoaAZHQJgHCuQp4KRoB03oA2gIR0CnxTnrQgLadX2UKGgGR0CVvFcGC7K8aAdN6ANoCEdAp8vrSqlxfnV9lChoBkdAkE7CT+vQnmgHTegDaAhHQKfP4nAIpph1fZQoaAZHQJckR1xKg7JoB03oA2gIR0Cn0D1vES/TdX2UKGgGR0CU7njL0SRKaAdN6ANoCEdAp9GMRlHz6XV9lChoBkdAl/xhf8dgfGgHTegDaAhHQKfYGhib2Dh1fZQoaAZHQJWwu/ag261oB03oA2gIR0Cn3B74Ju2rdX2UKGgGR0CKzlPM0P6LaAdN6ANoCEdAp9yEFSsKcHV9lChoBkdAkpvsbzbvgGgHTegDaAhHQKfd3y5I6Kd1fZQoaAZHwD/+DQJHAh1oB03oA2gIR0Cn5JE1/DtPdX2UKGgGR0B6+v7rLQokaAdN6ANoCEdAp+ikY4yXU3V9lChoBkdAb5Z/8VHnU2gHTegDaAhHQKfpAu7pV0d1fZQoaAZHQAKx28qWkadoB03oA2gIR0Cn6lH9vS+hdX2UKGgGR0CF04zTnaFmaAdN6ANoCEdAp/DvvnbItHV9lChoBkdAlRBMzQ/oq2gHTegDaAhHQKf1AyhzvJB1fZQoaAZHQJil3HEMspZoB03oA2gIR0Cn9WUTcqOMdX2UKGgGR0CUO7Gkep4saAdN6ANoCEdAp/atitq59XV9lChoBkdAlyo0pqh11WgHTegDaAhHQKf9P2saKk51fZQoaAZHQFtvV1Oj7ANoB03oA2gIR0CoAVMLF4s3dX2UKGgGR0CS1lJZW7voaAdN6ANoCEdAqAG3bVSXMXV9lChoBkdAkRBkQkHD8GgHTegDaAhHQKgDDrAP/aR1fZQoaAZHQJOAH0163RZoB03oA2gIR0CoCdfLcKw7dX2UKGgGR0ByBTCtRvWIaAdN6ANoCEdAqA3W2VmjCnV9lChoBkdAl1ZmNJe3QWgHTegDaAhHQKgOO5HVf/p1fZQoaAZHQJLD6oS+QEJoB03oA2gIR0CoD4S1Vo6CdX2UKGgGR0CR98I9kjHGaAdN6ANoCEdAqBYY/NZ/1HV9lChoBkdAkMKgC8vmHWgHTegDaAhHQKgaNgmZ3LV1fZQoaAZHQJLc39KmKqJoB03oA2gIR0CoGpMmv4dqdX2UKGgGR0CH/8bVjI7vaAdN6ANoCEdAqBvm4gA6uHV9lChoBkdAj+ju5jH4oWgHTegDaAhHQKgihMJx//h1fZQoaAZHQJHFffbblBBoB03oA2gIR0CoJnlm4AjqdX2UKGgGR0CRARg9Net0aAdN6ANoCEdAqCbZ20Re1XV9lChoBkdAX4olyBClamgHTegDaAhHQKgoILAHmih1fZQoaAZHQJINJaC+UQloB03oA2gIR0CoLqx7zCk5dX2UKGgGR0CNaW/nnuAqaAdN6ANoCEdAqDLb19ORDHV9lChoBkdAkfFt4VymymgHTegDaAhHQKgzOquKXOZ1fZQoaAZHQJNa7PdEb5xoB03oA2gIR0CoNRqwQlKLdX2UKGgGR0CRfXEfDDTCaAdN6ANoCEdAqDzlpyp71XV9lChoBkdAk/wKQNkOJGgHTegDaAhHQKhA6x46fap1fZQoaAZHQJFxqa2F36hoB03oA2gIR0CoQU/GEPDpdX2UKGgGR0CTach24d6taAdN6ANoCEdAqEKfrIHTqnV9lChoBkdAldPBnFo+OmgHTegDaAhHQKhJH9Sde6Z1fZQoaAZHQJEINVOsT39oB03oA2gIR0CoTSvc8DB/dX2UKGgGR0CXMG8ujASGaAdN6ANoCEdAqE2Pw3HaOHV9lChoBkdAk+uDewcHW2gHTegDaAhHQKhO2IyCWeJ1fZQoaAZHQJAwhXfZVXFoB03oA2gIR0CoVYAksz2wdX2UKGgGR0CL1QfT1CgLaAdN6ANoCEdAqFmYcDKYA3V9lChoBkdAlH9/tY0VJ2gHTegDaAhHQKhZ9n9Nvfl1fZQoaAZHQJJarVd5Y5loB03oA2gIR0CoW0Ml1KXfdX2UKGgGR0CSdjLRa5f/aAdN6ANoCEdAqGHUurZJ1HV9lChoBkdAkBA8xfv4NGgHTegDaAhHQKhl67EpAlh1fZQoaAZHQJPKebBoEjhoB03oA2gIR0CoZk9B8hLXdX2UKGgGR0CUQViX6ZYxaAdN6ANoCEdAqGeqySmqHXV9lChoBkdAkQLDujRD1GgHTegDaAhHQKhuIoybhFV1fZQoaAZHQJFFUMtsen1oB03oA2gIR0CociMZpBX0dX2UKGgGR0CFPMyY5T60aAdN6ANoCEdAqHKIaHbh33V9lChoBkdAkYVkI5YHPmgHTegDaAhHQKhz5Jmukk91fZQoaAZHQJKvRzzVc2RoB03oA2gIR0Coeom1pj+adX2UKGgGR0CTQbN21UlzaAdN6ANoCEdAqH7Ai9qUNnV9lChoBkdAk6rQ7o0Q9WgHTegDaAhHQKh/Hm4Ajpt1fZQoaAZHQJQQPRArxy5oB03oA2gIR0CogHKjrRjSdX2UKGgGR0CJxiqR2bG4aAdN6ANoCEdAqIcNfmcOLHV9lChoBkdAk2QNhZyMk2gHTegDaAhHQKiLErPt2LZ1fZQoaAZHQJJZu5tm+TNoB03oA2gIR0Coi24gRsdldX2UKGgGR0CN9uqjJuEVaAdN6ANoCEdAqIy9M495hXV9lChoBkdAcvvID5j6N2gHTegDaAhHQKiTbZf2K2t1fZQoaAZHQIf0SdFvybxoB03oA2gIR0Col3VNYbKidX2UKGgGR0B0ZmkbgjyGaAdN6ANoCEdAqJfVXLeQ+3V9lChoBkdAgog+bVjI72gHTegDaAhHQKiZI5hjOLR1fZQoaAZHQJRPSWZ7XxxoB03oA2gIR0Con8ircTJydX2UKGgGR0CTi+N9H+ZPaAdN6ANoCEdAqKPGJSBK+XVlLg=="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 62500, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "max_grad_norm": 0.5, "normalize_advantage": false, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa2765d7b64479bcb1edc323562fc77223e8c62d465b42cee662b6dade1cf1ec
+size 1136153

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1481.596170764463, "std_reward": 117.71741127228779, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-09T20:07:09.227314"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8c3af0c37e71b88168b8c31eb42e66e252a60dcabf8faea499a5bad8e425a804
+size 2521