Initial commit

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: 940.00 +/- 81.09
+      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:e3c1fec450624976bd3f30b94ea9431502ac5ccd3e8768d792ac2142f5f51cdf
+size 129195

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:": "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 0x7f1692cc4320>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1692cc43b0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1692cc4440>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1692cc44d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f1692cc4560>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f1692cc45f0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1692cc4680>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f1692cc4710>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1692cc47a0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1692cc4830>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1692cc48c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f1692d08c00>"
+    },
+    "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": 1667162691077614373,
+    "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:": "gASVRAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQI/wH9P1tfqMAWyUTegDjAF0lEdAqDS1vES/TXV9lChoBkdAi98dz4k/r2gHTegDaAhHQKg3CpKjBVN1fZQoaAZHQIw15trKvFFoB03oA2gIR0CoOiKcd5prdX2UKGgGR0COshZ/0/W2aAdN6ANoCEdAqDpd1yNn5HV9lChoBkdAj+0WoegctGgHTegDaAhHQKhB6nKnvUl1fZQoaAZHQI5/P49HMEBoB03oA2gIR0CoRCKPn0TUdX2UKGgGR0CPCtyLAHmjaAdN6ANoCEdAqEdBiy6cy3V9lChoBkdAkZS2gOBlMGgHTegDaAhHQKhHd9m6Gxl1fZQoaAZHQJHvaWJJoTRoB03oA2gIR0CoTwclHBk7dX2UKGgGR0CRL67oB7u2aAdN6ANoCEdAqFE50OmR/3V9lChoBkdAkGG7Y5DJEGgHTegDaAhHQKhUYrUb1h91fZQoaAZHQJA+ZQxesxRoB03oA2gIR0CoVJ2eQMhHdX2UKGgGR0CQv9dyksSTaAdN6ANoCEdAqFxh8jRlYnV9lChoBkdAjn4IN3GGVWgHTegDaAhHQKher557gKp1fZQoaAZHQJDE6e6I3zdoB03oA2gIR0CoYfZbpu/DdX2UKGgGR0CPQJJtix3WaAdN6ANoCEdAqGIyADq4Y3V9lChoBkdAkAz8VxjriWgHTegDaAhHQKhqDRTjvNN1fZQoaAZHQI+6XZkCmuVoB03oA2gIR0CobFN5D7ZWdX2UKGgGR0CRSPGfwqiHaAdN6ANoCEdAqG9z3AVO9HV9lChoBkdAjy3YT9KmK2gHTegDaAhHQKhvrNBWxQl1fZQoaAZHQI6bZSm65G1oB03oA2gIR0Cod4o9LYf5dX2UKGgGR0CMntdTHbRGaAdN6ANoCEdAqHnJ/wy6+XV9lChoBkdAkFdXaJyhjGgHTegDaAhHQKh85H09QoF1fZQoaAZHQI6Jm7HyVfNoB03oA2gIR0CofRuSGJvYdX2UKGgGR0CNHMo1k1/EaAdN6ANoCEdAqITPEdeY2XV9lChoBkdAiTfLaVUuMGgHTegDaAhHQKiHDzzVc2R1fZQoaAZHQIyVWvt+kQBoB03oA2gIR0CoikgiFCb+dX2UKGgGR0COA4MkyDZlaAdN6ANoCEdAqIp+/rSmZXV9lChoBkdAkJ75q7Ack2gHTegDaAhHQKiULIHTqjd1fZQoaAZHQI8ASLMs6JZoB03oA2gIR0ColoCXY150dX2UKGgGR0CMcjr9l2/0aAdN6ANoCEdAqJmjKifxt3V9lChoBkdAiH5w79ycTmgHTegDaAhHQKiZ3aQFLWZ1fZQoaAZHQI6nnwb2lEZoB03oA2gIR0CooX8JD3M7dX2UKGgGR0CPPTKxLTQWaAdN6ANoCEdAqKPHhsImgXV9lChoBkdAjv34rBj4H2gHTegDaAhHQKim77mdRSB1fZQoaAZHQIvtBPM0P6NoB03oA2gIR0CopyXSjQAudX2UKGgGR0CN0HUDMeOoaAdN6ANoCEdAqK678cdYGXV9lChoBkdAjYaZFw1iv2gHTegDaAhHQKixAHLzPKN1fZQoaAZHQI0CgC6pYLdoB03oA2gIR0CotCjej2zwdX2UKGgGR0CQXzITXarWaAdN6ANoCEdAqLRezWwu/XV9lChoBkdAjxgmU4aP0mgHTegDaAhHQKi71wvQF9t1fZQoaAZHQJEJsoiLVFxoB03oA2gIR0Covg513dKvdX2UKGgGR0CQJHIK+i8GaAdN6ANoCEdAqMEhnOB193V9lChoBkdAkAk4XTEzf2gHTegDaAhHQKjBXUkv9Lp1fZQoaAZHQI5kvsolUqBoB03oA2gIR0CoyPu0CzTndX2UKGgGR0CPUM9Zid8RaAdN6ANoCEdAqMtBLK3d9HV9lChoBkdAkeNwtapxWGgHTegDaAhHQKjOdaPjn3d1fZQoaAZHQJAhxoakyk9oB03oA2gIR0Cozrpwjt5VdX2UKGgGR0CPtEZBsyi3aAdN6ANoCEdAqNaZQtSQ5nV9lChoBkdAjjC98zAN5WgHTegDaAhHQKjY2cm0E5h1fZQoaAZHQJC4RafSQYFoB03oA2gIR0Co2/y9VWCFdX2UKGgGR0CPd03d9Dx9aAdN6ANoCEdAqNwz1wo9cXV9lChoBkdAkNnMT37DVGgHTegDaAhHQKjj4Za3Zwp1fZQoaAZHQJAvf2Cdz4loB03oA2gIR0Co5iNj0+TvdX2UKGgGR0CP38j1wo9caAdN6ANoCEdAqOlDEzfrKXV9lChoBkdAkXnD0cwQDmgHTegDaAhHQKjpezWPLgZ1fZQoaAZHQI2QS8an755oB03oA2gIR0Co8Q0HIIWydX2UKGgGR0CQjLRAKOT8aAdN6ANoCEdAqPMyzTnaFnV9lChoBkdAkSplqnFYMmgHTegDaAhHQKj2SCwr1/V1fZQoaAZHQJHlBO1v2oNoB03oA2gIR0Co9n8ry1/ldX2UKGgGR0CN+kg13t8eaAdN6ANoCEdAqP35p5/smnV9lChoBkdAkhkz0UXYUWgHTegDaAhHQKkAKjASFoN1fZQoaAZHQJJLaL876pJoB03oA2gIR0CpA0syad+YdX2UKGgGR0CRv0B55Z8saAdN6ANoCEdAqQODPOY6XHV9lChoBkdAkLsSeNDMNmgHTegDaAhHQKkLIo5PuXx1fZQoaAZHQJKjptNzr/toB03oA2gIR0CpDWH58BuGdX2UKGgGR0CTREAAQxvfaAdN6ANoCEdAqRCuqkuYhXV9lChoBkdAlBWCYG+sYGgHTegDaAhHQKkQ6eumrKh1fZQoaAZHQJQVhqesgdRoB03oA2gIR0CpGIZh8YygdX2UKGgGR0CTSDXOGCZnaAdN6ANoCEdAqRq8uHvc8HV9lChoBkdAlEEcq4H5amgHTegDaAhHQKkd3EwWWQh1fZQoaAZHQJIi7aSLZSNoB03oA2gIR0CpHhLhaTwEdX2UKGgGR0CQSTRgqmTDaAdN6ANoCEdAqSW9l9SdfHV9lChoBkdAkarpRO1v22gHTegDaAhHQKkn8Eg4ffZ1fZQoaAZHQJG1Y+bExZdoB03oA2gIR0CpKxzSThYOdX2UKGgGR0CQeEoOx0MgaAdN6ANoCEdAqStYvpQk5nV9lChoBkdAktHbUXpGF2gHTegDaAhHQKky0THsC1Z1fZQoaAZHQJJCEHoouwpoB03oA2gIR0CpNRLrgOz6dX2UKGgGR0CUCRjtoi9qaAdN6ANoCEdAqThL8gpz93V9lChoBkdAkaQ8uBczImgHTegDaAhHQKk4hAcDKYB1fZQoaAZHQJL4pcnmaH9oB03oA2gIR0CpQBOsT37DdX2UKGgGR0CTArsMiKR/aAdN6ANoCEdAqUJPKhcqv3V9lChoBkdAkee9pEhJRWgHTegDaAhHQKlFV0RODap1fZQoaAZHQJR9EOuq3mVoB03oA2gIR0CpRYzGYKIBdX2UKGgGR0CSV5stTUAlaAdN6ANoCEdAqU1mC2+fy3V9lChoBkdAkml2IGhVVGgHTegDaAhHQKlPsg3cYZV1fZQoaAZHQI8G5HXmNipoB03oA2gIR0CpUs/nnuAqdX2UKGgGR0CRWpAzpHI7aAdN6ANoCEdAqVMGyHEdenV9lChoBkdAkjt1OoHcDmgHTegDaAhHQKlajNSqEOB1fZQoaAZHQJFPXRD1GspoB03oA2gIR0CpXMFTFVDKdX2UKGgGR0CSFoF7laKUaAdN6ANoCEdAqV/Znxri2nV9lChoBkdAk8GG8ujASGgHTegDaAhHQKlgD5nlGPR1fZQoaAZHQJAUt2bG3nZoB03oA2gIR0CpZ8OARTS9dX2UKGgGR0CMV8d6LOzIaAdN6ANoCEdAqWn2ZZ0Sy3V9lChoBkdAkN8MVtXPq2gHTegDaAhHQKltEQBgeBB1fZQoaAZHQJLlN64UeuFoB03oA2gIR0CpbUSxRl6JdX2UKGgGR0COsFN5+pfhaAdN6ANoCEdAqXTDDl5nlHV9lChoBkdAkdRYSL61s2gHTegDaAhHQKl2/9itq591fZQoaAZHQIm3JSFXaJ1oB03oA2gIR0CpehAEdNnHdX2UKGgGR0CQ9UiqQzUJaAdN6ANoCEdAqXpFZ9uxbHVlLg=="
+    },
+    "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 sha256:56fd4cbcbbdfa803d75aa3e4086370ee086df2b397586a54d7e3b2c7fbe777c4
+size 56126

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e04de7d669bb49e012c14881afeebf279d78688a8a9b829694b310c572078b4e
+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-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.7.15
+Stable-Baselines3: 1.6.2
+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 0x7f1692cc4320>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1692cc43b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1692cc4440>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1692cc44d0>", "_build": "<function ActorCriticPolicy._build at 0x7f1692cc4560>", "forward": "<function ActorCriticPolicy.forward at 0x7f1692cc45f0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1692cc4680>", "_predict": "<function ActorCriticPolicy._predict at 0x7f1692cc4710>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1692cc47a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1692cc4830>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1692cc48c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f1692d08c00>"}, "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": 1667162691077614373, "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:": "gASVTQIAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSwRLHIaUaAOMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiiULAAQAAAAAAAPexRLYAAIA/AAAAAAAAAAAAAAAAAAAAAAAAAIDQcJ09AAAAAHPX2r8AAAAAfAAFvgAAAACiPO0/AAAAAGMSPT0AAAAAQbzcPwAAAACyguy8AAAAAFdQAMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAKgh+2AACAPwAAAAAAAAAAAAAAAAAAAAAAAACAled0vQAAAAD2GvO/AAAAAAcpoz0AAAAA3873PwAAAAClMBG+AAAAAEQF/T8AAAAAcee7PQAAAAAYtf+/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAI4OttQAAgD8AAAAAAAAAAAAAAAAAAAAAAAAAgBicjb0AAAAAU8TxvwAAAABdFXM9AAAAADb56j8AAAAAI7XxvQAAAABiCuE/AAAAAKmNfj0AAAAAm2jyvwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAPdZvbYAAIA/AAAAAAAAAAAAAAAAAAAAAAAAAIACjQI+AAAAAMho6r8AAAAAaUjMPQAAAACO79k/AAAAAKiiub0AAAAAFKzhPwAAAACWQQE+AAAAAOws8L8AAAAAAAAAAAAAAAAAAAAAAAAAAJR0lGIu"}, "_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-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.7.15", "Stable-Baselines3": "1.6.2", "PyTorch": "1.12.1+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 939.9977743649157, "std_reward": 81.0929963344062, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-10-30T21:43:42.193102"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9a0bced5dbd83f9424428e0c4cab04bfbfc89c46d6ebdfafa2ee612383259a57
+size 2763