Initial commit

.gitattributes

@@ -29,3 +29,4 @@ 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 +1,36 @@
+---
+library_name: stable-baselines3
+tags:
+- AntBulletEnv-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 1742.72 +/- 304.35
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: AntBulletEnv-v0
+      type: AntBulletEnv-v0
+---
+
+# **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:a4caf096dc517882cfcd453e84479d5947c241bbc0add0e82ba960692acea337
+size 129193

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.0

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,105 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__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 0x7f359faa2320>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f359faa23b0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f359faa2440>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f359faa24d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f359faa2560>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f359faa25f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f359faa2680>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f359faa2710>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f359faa27a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f359faa2830>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f359faa28c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f359faf15a0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gASVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/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": 1661978250.0688496,
+    "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:": "gASVjAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwSFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDBAAAAACUdJRiLg=="
+    },
+    "_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:": "gASVRAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQJtzS+evpyKMAWyUTegDjAF0lEdAp/fbEvTPSnV9lChoBkdAm4KNKqXF+GgHTegDaAhHQKf43d4Vym11fZQoaAZHQJvs4RxtHhFoB03oA2gIR0Cn+P++dsi0dX2UKGgGR0CckTQsPJ7taAdN6ANoCEdAp/76ofjjrHV9lChoBkdAmaQ2SdOIqWgHTegDaAhHQKgEQDPGACp1fZQoaAZHQJY3MkX1rZdoB03oA2gIR0CoBTOOCGvfdX2UKGgGR0CbwkUTcqOMaAdN6ANoCEdAqAVVR51Ng3V9lChoBkdAhebiXpnpS2gHTegDaAhHQKgLazLOiWV1fZQoaAZHQJaWbHIZIhBoB03oA2gIR0CoEMAbp/wzdX2UKGgGR0CGorpgTh5xaAdN6ANoCEdAqBG19YwIt3V9lChoBkdAk8HPo3aSLmgHTegDaAhHQKgR1+irT6V1fZQoaAZHQJNXohW5paloB03oA2gIR0CoF9HOB19wdX2UKGgGR0BuUzfNzKcNaAdN6ANoCEdAqB0I9LYf4nV9lChoBkdAmEApAMUh3mgHTegDaAhHQKgeA42jwhJ1fZQoaAZHQJjY/w/gR9RoB03oA2gIR0CoHiXFDOTrdX2UKGgGR0CWyFqKP4mDaAdN6ANoCEdAqCP6MvRJE3V9lChoBkdAmb9XIZIg/2gHTegDaAhHQKgpU4WDYiB1fZQoaAZHQJlbrXpW3jNoB03oA2gIR0CoKk6YE4ecdX2UKGgGR0CZXQqASWZ7aAdN6ANoCEdAqCpwao/A03V9lChoBkdAlJxyn1nM+2gHTegDaAhHQKgwVHvttyh1fZQoaAZHQJjUc6RyOrBoB03oA2gIR0CoNauSfUWmdX2UKGgGR0CU6A2C/XXiaAdN6ANoCEdAqDaqiEg4fnV9lChoBkdAmOyJ9mYjS2gHTegDaAhHQKg2zGhEjPh1fZQoaAZHQI3s/07KaG5oB03oA2gIR0CoPL+4kNWmdX2UKGgGR0Ccyjjv/io9aAdN6ANoCEdAqEIFKAavR3V9lChoBkdAm/MLJ0W/J2gHTegDaAhHQKhC/9RaX8h1fZQoaAZHQJTOkYYR/VloB03oA2gIR0CoQyGFBY3edX2UKGgGR0CbLQfu1F6SaAdN6ANoCEdAqEkkSZjQRnV9lChoBkdAnPjj0th/iGgHTegDaAhHQKhOjES/TLJ1fZQoaAZHQJrivzH0btJoB03oA2gIR0CoT4j15B1LdX2UKGgGR0CYQ/MZxaPkaAdN6ANoCEdAqE+pokAxSHV9lChoBkdAmu2hr30wrWgHTegDaAhHQKhVmFLWZqp1fZQoaAZHQI5sn05EMLFoB03oA2gIR0CoWvi1AqusdX2UKGgGR0CURfSofjjraAdN6ANoCEdAqFv0wN9YwXV9lChoBkdAmyiYbbUPQWgHTegDaAhHQKhcF1rZamp1fZQoaAZHQJuULIV/MGJoB03oA2gIR0CoYhiJO32FdX2UKGgGR0Cbd1NCqp97aAdN6ANoCEdAqGd6JfpljHV9lChoBkdAngPuwHJLd2gHTegDaAhHQKhod7AtWdV1fZQoaAZHQJ097bCaZx9oB03oA2gIR0CoaJpCSidrdX2UKGgGR0CeKV06o2n9aAdN6ANoCEdAqG6j0jC53HV9lChoBkdAmtn9zwMH8mgHTegDaAhHQKhz+vzvqkd1fZQoaAZHQJRYiz4UN8VoB03oA2gIR0CodP9hy8zzdX2UKGgGR0CbZZhGH58CaAdN6ANoCEdAqHUmv4dp7HV9lChoBkdAln4u9i+cpmgHTegDaAhHQKh7P/T9bX91fZQoaAZHQJUBTg88s+VoB03oA2gIR0CogKKR+z+ndX2UKGgGR0CdDX8GcFyJaAdN6ANoCEdAqIGtyHVPN3V9lChoBkdAmi0PFBIFvGgHTegDaAhHQKiB0BYFJQN1fZQoaAZHQGErtcnmaH9oB03oA2gIR0CoiTgnMMZxdX2UKGgGR0CWWlDDjzZpaAdN6ANoCEdAqI6V0q6OHXV9lChoBkdAmvlV4keIVWgHTegDaAhHQKiPjxhlUZN1fZQoaAZHQIo5CTGHYYloB03oA2gIR0Coj7BTOxB3dX2UKGgGR0CaVgTjebd8aAdN6ANoCEdAqJWf/7zkIXV9lChoBkdAlSgb/ffoBGgHTegDaAhHQKia68cMmWt1fZQoaAZHQJnGQacZtN1oB03oA2gIR0Com+gQxvehdX2UKGgGR0CY5HLZzxPPaAdN6ANoCEdAqJwMU0vXb3V9lChoBkdAmBb2NJe3QWgHTegDaAhHQKiiByNn5BV1fZQoaAZHQJckS1hLGrFoB03oA2gIR0Cop1c+aBqcdX2UKGgGR0CTTsIFNcnmaAdN6ANoCEdAqKhPAO8TSXV9lChoBkdAm5lb6YVqOGgHTegDaAhHQKiocSt/4It1fZQoaAZHQJsgpDfFaStoB03oA2gIR0CormiI1tO3dX2UKGgGR0CaVFWf9P1taAdN6ANoCEdAqLO7UTcqOXV9lChoBkdAm/CI0qH45GgHTegDaAhHQKi0uqCpWFN1fZQoaAZHQIrK+YQarFRoB03oA2gIR0CotNxxcVxkdX2UKGgGR0CY76i/wiJPaAdN6ANoCEdAqLrJnvlU63V9lChoBkdAmil+ZkTYd2gHTegDaAhHQKjANa24NI91fZQoaAZHQJhv0F8ohIRoB03oA2gIR0CowS0euFHsdX2UKGgGR0CUsej6vaDgaAdN6ANoCEdAqMFPFHavinV9lChoBkdAmcLZ9mYjS2gHTegDaAhHQKjHW/zreIl1fZQoaAZHQJ1bqQzUI9loB03oA2gIR0CozL1RtP56dX2UKGgGR0CXvsKNAC4jaAdN6ANoCEdAqM25pN9H+nV9lChoBkdAlw+eA7Ppp2gHTegDaAhHQKjN3Vmz0H11fZQoaAZHQJZN0dgfEGZoB03oA2gIR0Co09sir1dxdX2UKGgGR0CNnoPf8/D+aAdN6ANoCEdAqNkwbfgrH3V9lChoBkdAmHjjND+irWgHTegDaAhHQKjaLEBKcut1fZQoaAZHQJUvaTzND+loB03oA2gIR0Co2k4ZdfLLdX2UKGgGR0CV0h03Ov+waAdN6ANoCEdAqOBj1XeWOnV9lChoBkdAlf1rKRuCPWgHTegDaAhHQKjluPuogmt1fZQoaAZHQICpF2cJ+lVoB03oA2gIR0Co5r5a3ZwodX2UKGgGR0CAkJS9du50aAdN6ANoCEdAqObib4Ju23V9lChoBkdAlJ1rn5i3HGgHTegDaAhHQKjs75xiobZ1fZQoaAZHQI1KcSPEKmdoB03oA2gIR0Co8mkXcgyNdX2UKGgGR0BiGadOIqLCaAdN6ANoCEdAqPNtvXK8tnV9lChoBkdAmR2px7zClGgHTegDaAhHQKjzkFpwjt51fZQoaAZHQJNfGWD6FdtoB03oA2gIR0Co+aCf6Gg0dX2UKGgGR0CaDGKTSsr/aAdN6ANoCEdAqP8guAZsK3V9lChoBkdAkLlTCUHIIWgHTegDaAhHQKkAJNbkfcN1fZQoaAZHQJQIjVf/m1ZoB03oA2gIR0CpAEZ0r9VFdX2UKGgGR0CXzd8vEjxDaAdN6ANoCEdAqQZjyOJcgXV9lChoBkdAmc11RgqmTGgHTegDaAhHQKkLv5rP+n91fZQoaAZHQJrmVxbSqlxoB03oA2gIR0CpDL3AM2FWdX2UKGgGR0CbQLKfFrEcaAdN6ANoCEdAqQzfk7wKB3V9lChoBkdAm8EeWrwOOWgHTegDaAhHQKkS+3cYZVJ1fZQoaAZHQJxJeqhlDnhoB03oA2gIR0CpGGGkep4sdX2UKGgGR0CEkNHxSYPYaAdN6ANoCEdAqRlt9tuUEHV9lChoBkdAnI9D7Q9idGgHTegDaAhHQKkZj225QP91fZQoaAZHQJsMzKdQO4JoB03oA2gIR0CpH4jin5zpdX2UKGgGR0CZ2QwG4ZuRaAdN6ANoCEdAqSTfcgyM1nV9lChoBkdAl3l+W0JF9mgHTegDaAhHQKkl2ll9Sdh1fZQoaAZHQJoiUGB4D9xoB03oA2gIR0CpJf6Lfk3kdX2UKGgGR0CctWvM8ox6aAdN6ANoCEdAqSvne+Eh7nVlLg=="
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_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 sha256:61f3ccb2207cc9f429088baac5e944a189c46ae8129e48b91beb00c2a2375243
+size 56126

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f329d9bec44a94d5b400bb4c378ee5aa91aeb3efceb90822c0de32607a3e235f
+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-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
+Python: 3.7.13
+Stable-Baselines3: 1.6.0
+PyTorch: 1.12.1+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7f359faa2320>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f359faa23b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f359faa2440>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f359faa24d0>", "_build": "<function ActorCriticPolicy._build at 0x7f359faa2560>", "forward": "<function ActorCriticPolicy.forward at 0x7f359faa25f0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f359faa2680>", "_predict": "<function ActorCriticPolicy._predict at 0x7f359faa2710>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f359faa27a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f359faa2830>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f359faa28c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f359faf15a0>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gASVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/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": 1661978250.0688496, "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:": "gASVjAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwSFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDBAAAAACUdJRiLg=="}, "_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:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_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-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.6.0", "PyTorch": "1.12.1+cu113", "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 sha256:683257d1af69ae53ebdc3f392b400fb32955742ac14dcb58aacdf56e73855a26
+size 1103162

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1742.7182990432746, "std_reward": 304.35125384319116, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-08-31T22:00:14.586254"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:39209b021a8dc48ede6175c0d79d98917fcdb6f8e8260a0b1fed447fee410a25
+size 2763