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: 1428.00 +/- 88.05
+      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 sha256:4cdd7b57ae09629caa1c4926313a3d7a51b95a7fa00d854113b7215b83061ec1
+size 129069

a2c-AntBulletEnv-v0/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

a2c-AntBulletEnv-v0/data

@@ -0,0 +1,106 @@
+{
+    "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 0x15611b820>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x15611b8b0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x15611b940>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x15611b9d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x15611ba60>",
+        "forward": "<function ActorCriticPolicy.forward at 0x15611baf0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x15611bb80>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x15611bc10>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x15611bca0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x15611bd30>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x15611bdc0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x15611be50>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x156105a80>"
+    },
+    "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": 1678329261726170000,
+    "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,
+    "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
+}

a2c-AntBulletEnv-v0/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e1962b299b60e1173175d043907f6a85cd7a4da50e552ce5036d475001639115
+size 56062

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2584a36660fdcb8085a301cea3002f855295d3a6ad9cc4edfbe8692e4912d18a
+size 56830

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: macOS-13.2-arm64-arm-64bit Darwin Kernel Version 22.3.0: Thu Jan  5 20:48:54 PST 2023; root:xnu-8792.81.2~2/RELEASE_ARM64_T6000
+- Python: 3.8.15
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1
+- GPU Enabled: False
+- Numpy: 1.24.1
+- 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 0x15611b820>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x15611b8b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x15611b940>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x15611b9d0>", "_build": "<function ActorCriticPolicy._build at 0x15611ba60>", "forward": "<function ActorCriticPolicy.forward at 0x15611baf0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x15611bb80>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x15611bc10>", "_predict": "<function ActorCriticPolicy._predict at 0x15611bca0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x15611bd30>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x15611bdc0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x15611be50>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x156105a80>"}, "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": 1678329261726170000, "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, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVRAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQJQzIPNFBpqMAWyUTegDjAF0lEdAst21BHCoCXV9lChoBkdAl5Ahm9QGfWgHTegDaAhHQLLdwR3eN1h1fZQoaAZHQJokhw71ZkloB03oA2gIR0Cy3gk0aZQYdX2UKGgGR0CY3705EMLGaAdN6ANoCEdAst4bjOs1bnV9lChoBkdAm27nQY1pCmgHTegDaAhHQLLfhnYxtYV1fZQoaAZHQJhYa68QI2RoB03oA2gIR0Cy35KRZEDydX2UKGgGR0CYU7SMLncMaAdN6ANoCEdAst/aXMQmNXV9lChoBkdAmh9MlsxfwGgHTegDaAhHQLLf7MdtEXt1fZQoaAZHQJNjNeTmnwZoB03oA2gIR0Cy4VeTeO4odX2UKGgGR0CXFWkf9xZMaAdN6ANoCEdAsuFjWy1NQHV9lChoBkdAlx1FxXGOuWgHTegDaAhHQLLhq4Hooux1fZQoaAZHQJrXs6IWP91oB03oA2gIR0Cy4b6jFhoedX2UKGgGR0CTzY9deIEbaAdN6ANoCEdAsuMsvXbudHV9lChoBkdAYHkEi+tbLWgHTegDaAhHQLLjOQ40dil1fZQoaAZHQJT2+I0qH45oB03oA2gIR0Cy44HeFcptdX2UKGgGR0CYjtF49ovjaAdN6ANoCEdAsuOUd7v5QHV9lChoBkdAl1BoDDCP62gHTegDaAhHQLLlAIeo1k11fZQoaAZHQJlA1bHIZIhoB03oA2gIR0Cy5QxmPHT7dX2UKGgGR0CYGOLteD3/aAdN6ANoCEdAsuVWur6tT3V9lChoBkdAmfPj+JgssmgHTegDaAhHQLLlaRsdkrh1fZQoaAZHQJhkF9kSVW1oB03oA2gIR0Cy5tRXjlxPdX2UKGgGR0CZn9UDMeOoaAdN6ANoCEdAsubgOXmeUnV9lChoBkdAkvo7w8W9DmgHTegDaAhHQLLnKPNmlIp1fZQoaAZHQJRtGKZUkv9oB03oA2gIR0Cy5zuHSF4+dX2UKGgGR0CVMxdcjZ+QaAdN6ANoCEdAsuiqAYpDu3V9lChoBkdAmeaBkEs8PmgHTegDaAhHQLLoti+cpb51fZQoaAZHQJdw0tkFwDNoB03oA2gIR0Cy6P7piZv2dX2UKGgGR0CRZbVObiIdaAdN6ANoCEdAsukR6F/QSnV9lChoBkdAkXR25Yoy9GgHTegDaAhHQLLqgP+n62x1fZQoaAZHQIYKMCcPOIJoB03oA2gIR0Cy6o0dq+JxdX2UKGgGR0CXlifixVyWaAdN6ANoCEdAsurXJkoWpXV9lChoBkdAhzZWxIJ7cGgHTegDaAhHQLLq6b5M10l1fZQoaAZHQJZ8Cdat9x9oB03oA2gIR0Cy7FViONo8dX2UKGgGR0CS+riNbTttaAdN6ANoCEdAsuxhbfP5YnV9lChoBkdAl+BOuq3mWGgHTegDaAhHQLLsqsV+I/J1fZQoaAZHQJme3LW7OFBoB03oA2gIR0Cy7L20Re1KdX2UKGgGR0CYr8beMyaeaAdN6ANoCEdAsu4sNpdrwnV9lChoBkdAluQnIp6QeWgHTegDaAhHQLLuOHJLdvd1fZQoaAZHQJbd02Q4jr1oB03oA2gIR0Cy7oDo+wC9dX2UKGgGR0CVaZXxvvSdaAdN6ANoCEdAsu6TuDzy0HV9lChoBkdAmJ2apo9LYmgHTegDaAhHQLLv/nYQJ5V1fZQoaAZHQJd951ZDArRoB03oA2gIR0Cy8ApBC2MLdX2UKGgGR0CZXVqEeyRkaAdN6ANoCEdAsvBSnbZezHV9lChoBkdAmYd/WpZOi2gHTegDaAhHQLLwZRRMvh91fZQoaAZHQJhypiYsunNoB03oA2gIR0Cy8dB3NcGDdX2UKGgGR0CbNjLdvbXZaAdN6ANoCEdAsvHcma6ST3V9lChoBkdAlIvE52hZhmgHTegDaAhHQLLyJP3SKFZ1fZQoaAZHQJUVJwl0HQhoB03oA2gIR0Cy8jgO4G2UdX2UKGgGR0CVaxVII4VAaAdN6ANoCEdAsvOlkiD/VHV9lChoBkdAmNWhREWqLmgHTegDaAhHQLLzsXFtKqZ1fZQoaAZHQJj7A8dPtUpoB03oA2gIR0Cy8/q9CeEqdX2UKGgGR0CWrk0mdAgQaAdN6ANoCEdAsvQNMPBi1HV9lChoBkdAmLgzWbwz+GgHTegDaAhHQLL1d5zo2XN1fZQoaAZHQJkmF86V+qloB03oA2gIR0Cy9YPzFuNxdX2UKGgGR0CV/iC4SYgJaAdN6ANoCEdAsvXMEGJN03V9lChoBkdAlo0HUDuBtmgHTegDaAhHQLL13s6q8151fZQoaAZHQJmfNaaCtihoB03oA2gIR0Cy90rKNhmYdX2UKGgGR0CXvAI6bONYaAdN6ANoCEdAsvdWukk8inV9lChoBkdAlOCYISlFdGgHTegDaAhHQLL3ny1uzhR1fZQoaAZHQJeDou27Wd5oB03oA2gIR0Cy97Gn0kGBdX2UKGgGR0CXrqZG8VYZaAdN6ANoCEdAsvkfYBeXzHV9lChoBkdAi7qSM1jy4GgHTegDaAhHQLL5K8b70nR1fZQoaAZHQJXpZZntfHBoB03oA2gIR0Cy+XXGS6lMdX2UKGgGR0CUbWMCcPOIaAdN6ANoCEdAsvmITpPhynV9lChoBkdAmZ6jsMRYimgHTegDaAhHQLL68J6po9N1fZQoaAZHQJh7BkvsZ51oB03oA2gIR0Cy+vxo7FKkdX2UKGgGR0CbRclRxcVyaAdN6ANoCEdAsvtFXvH933V9lChoBkdAmiOWSpzcRGgHTegDaAhHQLL7WB4D9wZ1fZQoaAZHQJnlFISUTtdoB03oA2gIR0Cy/MKHKwIMdX2UKGgGR0Caz1BnBciXaAdN6ANoCEdAsvzOi0v4/XV9lChoBkdAmPCh1cMVlGgHTegDaAhHQLL9FuAI6bR1fZQoaAZHQJuP1tsN2DBoB03oA2gIR0Cy/SlvhqCZdX2UKGgGR0CVWRWPcSGraAdN6ANoCEdAsv6QN3GGVXV9lChoBkdAmqF5DVpblmgHTegDaAhHQLL+m+xnnMd1fZQoaAZHQJjlMTJyQxNoB03oA2gIR0Cy/uMp9ZzQdX2UKGgGR0CT9SJxvNu+aAdN6ANoCEdAsv71pXZGrnV9lChoBkdAmHgLiqABk2gHTegDaAhHQLMAYZc9nsd1fZQoaAZHQJtDSzY287JoB03oA2gIR0CzAG4LkS26dX2UKGgGR0Ca4BmWMS9NaAdN6ANoCEdAswC2OQyRCHV9lChoBkdAmTVgCGN70GgHTegDaAhHQLMAyNiH6/J1fZQoaAZHQJgqWUSqU/xoB03oA2gIR0CzAjPf8/D+dX2UKGgGR0CW0X2OyVv/aAdN6ANoCEdAswI/17IDHXV9lChoBkdAmQVgZwXIl2gHTegDaAhHQLMCiY+jdpJ1fZQoaAZHQJji56Y3Ns5oB03oA2gIR0CzApyXIEKWdX2UKGgGR0CVs591U2k0aAdN6ANoCEdAswQHdsSCe3V9lChoBkdAlrui6+WWyGgHTegDaAhHQLMEE7Ciypt1fZQoaAZHQJaQVhfBvaVoB03oA2gIR0CzBFuqJdjYdX2UKGgGR0CXEqwcYIjXaAdN6ANoCEdAswRuNPxhD3V9lChoBkdAl0Me5nUUf2gHTegDaAhHQLMF26ZYxL11fZQoaAZHQJjhqgzxgApoB03oA2gIR0CzBeeU6gdwdX2UKGgGR0CYn6MotthvaAdN6ANoCEdAswYwht+CsnV9lChoBkdAl8UO7xusLmgHTegDaAhHQLMGQwrUb1h1fZQoaAZHQJoou/QBxPxoB03oA2gIR0CzB66xs2vTdX2UKGgGR0CcM0deIEbHaAdN6ANoCEdAswe623KB/nV9lChoBkdAm2eYc3l0YGgHTegDaAhHQLMIAzMzMzN1fZQoaAZHQJlrxefI0ZZoB03oA2gIR0CzCBXj6vaDdX2UKGgGR0CVM4VC5VfeaAdN6ANoCEdAswmCcZtNz3V9lChoBkdAmFGrNGEwnGgHTegDaAhHQLMJjkona391fZQoaAZHQJhth+EytV9oB03oA2gIR0CzCdZnQID6dX2UKGgGR0CZB62/i5uqaAdN6ANoCEdAswnpAAyVOnVlLg=="}, "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": "macOS-13.2-arm64-arm-64bit Darwin Kernel Version 22.3.0: Thu Jan  5 20:48:54 PST 2023; root:xnu-8792.81.2~2/RELEASE_ARM64_T6000", "Python": "3.8.15", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1", "GPU Enabled": "False", "Numpy": "1.24.1", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8ccbc9c3c760cb3d28e04a0a2e60e175d429153804a2a616b509c6218acdf532
+size 1206904

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1427.9968623968773, "std_reward": 88.05186547653457, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-09T15:33:52.612101"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:98fb8082894fe37b99e95aca8d45e402b1ac7805ed94bfb9d83df6998a185d46
+size 2136