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: 483.97 +/- 138.04
+      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 sha256:25382938ef4e6284d35bb44c21920e5b5b3567eb9fbe5f532922a29766c050f1
+size 129223

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,107 @@
+{
+    "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 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 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 ActorCriticPolicy.__init__ at 0x7fbfa6cffdc0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbfa6cffe50>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbfa6cffee0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbfa6cfff70>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fbfa6d02040>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fbfa6d020d0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fbfa6d02160>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbfa6d021f0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fbfa6d02280>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbfa6d02310>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbfa6d023a0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbfa6d02430>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fbfa6d034c0>"
+    },
+    "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
+        }
+    },
+    "num_timesteps": 2000000,
+    "_total_timesteps": 2000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1681314222629253209,
+    "learning_rate": 0.00096,
+    "tensorboard_log": null,
+    "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,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "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,
+    "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
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d42d27502901404c32539f4d61127ef5372d431e0b1671e19b255a58a0ad03d
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:708367dae2b99cfa17be6886d532fca86c145d2c54df88dd60afab364968dd77
+size 56894

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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.0+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- 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 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 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 ActorCriticPolicy.__init__ at 0x7fbfa6cffdc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbfa6cffe50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbfa6cffee0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbfa6cfff70>", "_build": "<function ActorCriticPolicy._build at 0x7fbfa6d02040>", "forward": "<function ActorCriticPolicy.forward at 0x7fbfa6d020d0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fbfa6d02160>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbfa6d021f0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fbfa6d02280>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbfa6d02310>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbfa6d023a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbfa6d02430>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fbfa6d034c0>"}, "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}}, "num_timesteps": 2000000, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1681314222629253209, "learning_rate": 0.00096, "tensorboard_log": null, "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, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVQAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQI4dLNOdoWaMAWyUTegDjAF0lEdAr0apVOsT4HV9lChoBkdAkMAru+h4+2gHTegDaAhHQK9K7/qgRK91fZQoaAZHQJEvTR0EHMVoB03oA2gIR0CvUD8TBZZCdX2UKGgGR0CQFNn2qT8paAdN6ANoCEdAr1Kkb961LXV9lChoBkdAk3UJ+QU5/GgHTegDaAhHQK9TPdzGPxR1fZQoaAZHQJFs1ChN/ONoB03oA2gIR0CvV3xKpT/AdX2UKGgGR0CQxw3SKFZgaAdN6ANoCEdAr17/XAdn03V9lChoBkdAgM0t1IRRM2gHTegDaAhHQK9ij1bqyGB1fZQoaAZHQIksVYISlFdoB03oA2gIR0CvYxzFl05mdX2UKGgGR0BzxOOearmyaAdN6ANoCEdAr2eJBE8aGnV9lChoBkdAdq68FpwjuGgHTegDaAhHQK9s28oQWep1fZQoaAZHQIu/UnRb8m9oB03oA2gIR0Cvb0Zle4TcdX2UKGgGR0CHIV8QZn+RaAdN6ANoCEdAr2/VDlYEGXV9lChoBkdAbmcL9deIEmgHTegDaAhHQK90JqWTouB1fZQoaAZHQIJaW+M6zVtoB03oA2gIR0Cveqw7kn1GdX2UKGgGR0CLrfuVopQUaAdN6ANoCEdAr35wzrNW2nV9lChoBkdAiZXARChN/WgHTegDaAhHQK9/TjhDPWx1fZQoaAZHQIIeV+PRzBBoB03oA2gIR0Cvg89Mj/uLdX2UKGgGR0CKuJIZIg/1aAdN6ANoCEdAr4jdR51Ng3V9lChoBkdAhx/JcophF2gHTegDaAhHQK+LR7yhBZ91fZQoaAZHQIZVVPP9kz5oB03oA2gIR0Cvi9bfYSQHdX2UKGgGR0CI1IL5ylvZaAdN6ANoCEdAr5BHcDbJwXV9lChoBkdAgg7N2C/XXmgHTegDaAhHQK+WHsa86FN1fZQoaAZHQHjnJ1JUYKpoB03oA2gIR0Cvmcuv+wTudX2UKGgGR0CCYVYQrc0taAdN6ANoCEdAr5q6oESuhnV9lChoBkdAhqNhTwUg0WgHTegDaAhHQK+gmqZML4N1fZQoaAZHQINbty925hBoB03oA2gIR0CvpfIBJZntdX2UKGgGR0CADZ4W1twaaAdN6ANoCEdAr6hkYQ8OkXV9lChoBkdAiHq4D9wWFmgHTegDaAhHQK+o8Kohpxp1fZQoaAZHQIMtqS3b215oB03oA2gIR0CvrUzpHI6sdX2UKGgGR0CFdddsSCe3aAdN6ANoCEdAr7KmsDGLk3V9lChoBkdAkFNVSjxkNGgHTegDaAhHQK+2P4sVclh1fZQoaAZHQIXoQaP0Zm9oB03oA2gIR0CvtxU29+PSdX2UKGgGR0B0g/UBnzxxaAdN6ANoCEdAr72wWHk92XV9lChoBkfAPRBbSqlxfmgHS2VoCEdAr774NutOmHV9lChoBkfAP8/Xf642CWgHS1loCEdAr8Ae/+Kjz3V9lChoBkdAdT4pX6qKg2gHTegDaAhHQK/C9ARkEs91fZQoaAZHQHPGeU+s5n1oB03oA2gIR0CvxWXH7xd6dX2UKGgGR0CHpdUWEbo9aAdN6ANoCEdAr8X5BJI1+HV9lChoBkdAawEhf0Eov2gHTegDaAhHQK/MzAt4A0d1fZQoaAZHQHQ+ZntfG+9oB03oA2gIR0Cvz8bXHzYmdX2UKGgGR0Bt4bG5tm+TaAdN6ANoCEdAr9LnLzPKMnV9lChoBkdAhF4G5DqnnGgHTegDaAhHQK/TtER8MNN1fZQoaAZHQHLmQZ0jkdVoB03oA2gIR0Cv3T4zrNW3dX2UKGgGR0CFLuZ6Uqx1aAdN6ANoCEdAr+AYgLZzxXV9lChoBkdAhgbMLfDUE2gHTegDaAhHQK/io9KVY6p1fZQoaAZHQIjoPdl/YrdoB03oA2gIR0Cv4zbDuSfUdX2UKGgGR0CIO2qZtvXLaAdN6ANoCEdAr+neo5xR23V9lChoBkdAhGtdsSCe3GgHTegDaAhHQK/sqgKWszV1fZQoaAZHQIUg8h5gPVdoB03oA2gIR0Cv7yhK15SndX2UKGgGR0CDEdWPLgXNaAdN6ANoCEdAr+/1f1Hvt3V9lChoBkdAeN7G1x82JmgHTegDaAhHQK/6DfNRm9R1fZQoaAZHQHH97cfvF3poB03oA2gIR0Cv/QI3zcyndX2UKGgGR0B88HHmzSkTaAdN6ANoCEdAr/+Ff/m1Y3V9lChoBkdAasQSDAaegGgHTegDaAhHQLAADKvmozh1fZQoaAZHQFM20xM36yloB026AmgIR0CwAYhPO6d2dX2UKGgGR0CA4hgtvn8saAdN6ANoCEdAsATeuA7Pp3V9lChoBkdAceuHWBjFymgHTegDaAhHQLAGM9NN8E51fZQoaAZHQIvuqTMaCMBoB03oA2gIR0CwBnvyf+S9dX2UKGgGR0CLbVxe9i+daAdN6ANoCEdAsAh8ANoak3V9lChoBkdAaZRhOP/7zmgHTegDaAhHQLAM+hw2l2x1fZQoaAZHQIFvXCdjG1hoB03oA2gIR0CwDieG9HtndX2UKGgGR0B3Z0Ka5PM0aAdN6ANoCEdAsA5tGz8gp3V9lChoBkdAdezr4nF5wGgHTegDaAhHQLAP8N9ph4N1fZQoaAZHP9U189fTkQxoB0sVaAhHQLAQEgoPTXt1fZQoaAZHQHfe2CROk+JoB03oA2gIR0CwEzieyzHCdX2UKGgGR0B4TEFY+0PZaAdN6ANoCEdAsBR0vkBCD3V9lChoBkdAgonXDvVmSWgHTegDaAhHQLAUvFLWZqp1fZQoaAZHQI/EeLgn+hpoB03oA2gIR0CwFpPRqoIfdX2UKGgGR0CNJF8BuGbkaAdN6ANoCEdAsBsdLi++NHV9lChoBkdAjfvrvb48EGgHTegDaAhHQLAcV/s3Q2N1fZQoaAZHQIjpsQumJnBoB03oA2gIR0CwHKEU0vXcdX2UKGgGR0CKEIdGy5ZsaAdN6ANoCEdAsB4iCROk+HV9lChoBkdAi1FHdXT3I2gHTegDaAhHQLAhN8cuJ1t1fZQoaAZHQI2lBW5paidoB03oA2gIR0CwImlEE1VHdX2UKGgGR0CQcJ7ZnL7oaAdN6ANoCEdAsCKyOwPiDXV9lChoBkdAjKi0Gu9vj2gHTegDaAhHQLAkPu3MINV1fZQoaAZHQI34MmBvrGBoB03oA2gIR0CwKIIHTqjadX2UKGgGR0CMaFaoMrmRaAdN6ANoCEdAsCo3jMmnfnV9lChoBkdAkb2nVbzK92gHTegDaAhHQLAqemAbyYp1fZQoaAZHQJP/AxWT5ftoB03oA2gIR0CwLAtGqgh9dX2UKGgGR0CUgVPatcOcaAdN6ANoCEdAsC8W9pRGdHV9lChoBkdAhTQF1KXfImgHTegDaAhHQLAwRYjSofl1fZQoaAZHQJDLjTCtRvZoB03oA2gIR0CwMIkSuhbodX2UKGgGR0CL5rbGm1pkaAdN6ANoCEdAsDIeWfK6nXV9lChoBkdAhlflN1yNoGgHTd4CaAhHQLA1b1KoQ4F1fZQoaAZHQIubZR8+ialoB03oA2gIR0CwNahN/OMVdX2UKGgGR0B8B9MlC1JEaAdN6ANoCEdAsDd8wUQCjnV9lChoBkdAheSB3qzJIWgHTegDaAhHQLA5y/2TPjZ1fZQoaAZHQGk9NwrDqGFoB0v/aAhHQLA7YJT2nKp1fZQoaAZHQH5faYAsCkpoB03oA2gIR0CwPMN61LJ0dX2UKGgGR0CBy2E6DGtIaAdN6ANoCEdAsDzwJkXk53V9lChoBkdAfaCxCY1HfGgHTegDaAhHQLA+E/X5FgF1fZQoaAZHQIGX0vM8ox5oB027AmgIR0CwP5Fgx8D0dX2UKGgGR0B1etwkxASnaAdNzAFoCEdAsEDM54nndXV9lChoBkdAa06oc7yQP2gHTQ8BaAhHQLBBKemelKt1fZQoaAZHQG0RH5Jsfq5oB008AWgIR0CwQqvzBhx6dX2UKGgGR0CGGoz2vjffaAdN6ANoCEdAsEKvz+WGAXV9lChoBkdAfrd4nF5v+GgHTegDaAhHQLBC2MqjJuF1fZQoaAZHQGqOIg3cYZVoB00/AWgIR0CwRaBR/EwWdWUu"}, "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, "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, "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": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 483.97049542093885, "std_reward": 138.04197220933852, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-12T16:47:54.981732"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1bb32202afcc814f10d4099b9a963425a32fb5e4b2af8cbb57f9020dcb67a376
+size 2170