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: 1296.76 +/- 221.92
+      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 sha256:25ecb049082d7a44a542ad4e7945eb9847396f0a04787470ccfd7816a0af621a
+size 129260

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 0x7f695632ad30>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f695632adc0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f695632ae50>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f695632aee0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f695632af70>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f695632d040>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f695632d0d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f695632d160>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f695632d1f0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f695632d280>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f695632d310>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f695632d3a0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f69563267b0>"
+    },
+    "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:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAIC/AACAvwAAgL8AAIC/AACAvwAAgL8AAIC/AACAv5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIA/AACAPwAAgD8AAIA/AACAPwAAgD8AAIA/AACAP5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAQEBAQEBAQGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAEBAQEBAQEBlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu",
+        "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": 1675187382927833119,
+    "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 sha256:2e81e751e12b240dde913ef600c561696abf0a261e5bfc0f427a545d0b95639d
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:111cccdec8804b44bdcfd3aae86a9d2fd3c17187de400c55685ac39ef580130a
+size 56958

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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+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 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 0x7f695632ad30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f695632adc0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f695632ae50>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f695632aee0>", "_build": "<function ActorCriticPolicy._build at 0x7f695632af70>", "forward": "<function ActorCriticPolicy.forward at 0x7f695632d040>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f695632d0d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f695632d160>", "_predict": "<function ActorCriticPolicy._predict at 0x7f695632d1f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f695632d280>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f695632d310>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f695632d3a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f69563267b0>"}, "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": 1675187382927833119, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/T3UQTVUdaYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_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:": "gAWVRAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQJYk7K+zt1KMAWyUTegDjAF0lEdArPQ+1fE4vXV9lChoBkdAk05KJl8PWmgHTegDaAhHQKz1ZNZeRgZ1fZQoaAZHQJdAZ5zHS4RoB03oA2gIR0Cs/Chky1u0dX2UKGgGR0CUnYtJWeYlaAdN6ANoCEdArPww/cFhX3V9lChoBkdAkp2MK5TZQGgHTegDaAhHQK0AiyUs4DN1fZQoaAZHQJb6+01IiC9oB03oA2gIR0CtAa1v/BFedX2UKGgGR0CQ/lrFOwgUaAdN6ANoCEdArQiB+H8CP3V9lChoBkdAh9ALqt5lfGgHTegDaAhHQK0Iim4y44J1fZQoaAZHQJRmfpqynk1oB03oA2gIR0CtDPMEidJ8dX2UKGgGR0CNSBFGXokiaAdN6ANoCEdArQ4M5fdAPnV9lChoBkdAkk8D6BRQ8GgHTegDaAhHQK0U0scyWRl1fZQoaAZHQJBL1g2Ifr9oB03oA2gIR0CtFNwpF1B/dX2UKGgGR0CQDk3kxREXaAdN6ANoCEdArRlX/95yEXV9lChoBkdAkddyntOVPmgHTegDaAhHQK0agZUDMeR1fZQoaAZHQJGHfDNyHVRoB03oA2gIR0CtIWr0Bfa6dX2UKGgGR0CSJXNYr8R+aAdN6ANoCEdArSF0Cgbp/3V9lChoBkdAkGInRXwLE2gHTegDaAhHQK0l7RWLgoB1fZQoaAZHQJAd1MSK3uxoB03oA2gIR0CtJxHxJ/XodX2UKGgGR0CQQtwblzU7aAdN6ANoCEdArS3JvR7Z4HV9lChoBkdAkNDmozeoDWgHTegDaAhHQK0t0s6q8151fZQoaAZHQIiNcOuq3mVoB03oA2gIR0CtMlGzKLbYdX2UKGgGR0CQJ4Ay2x6faAdN6ANoCEdArTNspmVZ93V9lChoBkdAkpWrqt5lfGgHTegDaAhHQK06IMsH0K91fZQoaAZHQI95wJPZZjhoB03oA2gIR0CtOirmhdt3dX2UKGgGR0CNOgEnssxxaAdN6ANoCEdArT6PM0P6K3V9lChoBkdAkJ1dhAnlXGgHTegDaAhHQK0/t7/n4fx1fZQoaAZHQI0ZSoqCpWFoB03oA2gIR0CtRokVFhG6dX2UKGgGR0CR7D0XgtOEaAdN6ANoCEdArUaRzJZGKHV9lChoBkdAkNXx+WnjyWgHTegDaAhHQK1LB5uZThp1fZQoaAZHQJE4KL5ylvZoB03oA2gIR0CtTBzcZccEdX2UKGgGR0CO9wGbCrLhaAdN6ANoCEdArVK9x2jfvXV9lChoBkdAjwxc4gieNGgHTegDaAhHQK1SxuUliSd1fZQoaAZHQItK1ObiIcloB03oA2gIR0CtVzWOyVv/dX2UKGgGR0CTooaxoqTbaAdN6ANoCEdArVhX3+MqBnV9lChoBkdAkZJ0CeVcEGgHTegDaAhHQK1fJalk6Lh1fZQoaAZHQI8rf7JnxrloB03oA2gIR0CtXy5lnRLLdX2UKGgGR0CQNGFpwjt5aAdN6ANoCEdArWOw7/4qPXV9lChoBkdAh3iadUbT+mgHTegDaAhHQK1k0OI68xt1fZQoaAZHQJCOacH4XXRoB03oA2gIR0Cta69APd2xdX2UKGgGR0B2VncHnlnzaAdN6ANoCEdArWu4j8k2P3V9lChoBkdAih6ib+cYqGgHTegDaAhHQK1wDbGm1pl1fZQoaAZHQIuSFvMr3CdoB03oA2gIR0CtcSZIH1OCdX2UKGgGR0CQ67fa6BiDaAdN6ANoCEdArXgDmhdt23V9lChoBkdAjAOI0ALiM2gHTegDaAhHQK14DKuB+Wp1fZQoaAZHQJSSTO/tY0VoB03oA2gIR0CtfI3oC+10dX2UKGgGR0CIfDuJDVpcaAdN6ANoCEdArX2xMvh60XV9lChoBkdAkS6cv7FbV2gHTegDaAhHQK2EcRqXWvt1fZQoaAZHQJPnrrKNhmZoB03oA2gIR0CthHoU8FINdX2UKGgGR0CP7vk7OmiyaAdN6ANoCEdArYjkH4XXRXV9lChoBkdAlfbgrpaA4GgHTegDaAhHQK2KD/m1YyR1fZQoaAZHQJQ7zL5hz/9oB03oA2gIR0CtkNItthuwdX2UKGgGR0CQNxM0gr6MaAdN6ANoCEdArZDa7I1cdHV9lChoBkdAhQhHLq2SdWgHTegDaAhHQK2VVZ13dKx1fZQoaAZHQIL5Rk5IYm9oB03oA2gIR0CtlnfYzzmPdX2UKGgGR0CTttQnQY1paAdN6ANoCEdArZ0/BxgiNnV9lChoBkdAkgulK9PDYWgHTegDaAhHQK2dRy08eS11fZQoaAZHQJOfDMdLg4xoB03oA2gIR0Ctoa7ihnJ1dX2UKGgGR0CUi3BacI7eaAdN6ANoCEdAraLVa0QbuXV9lChoBkdAkFx+3MINVmgHTegDaAhHQK2poNT987Z1fZQoaAZHQJScHVPN3W5oB03oA2gIR0CtqamjTKDDdX2UKGgGR0BxPtuEVWS2aAdN6ANoCEdAra4JxJd0JXV9lChoBkdAlRRgK8cuJ2gHTegDaAhHQK2vMyDZlFt1fZQoaAZHQJT1uHi3ocJoB03oA2gIR0CtteDBl+VkdX2UKGgGR0CQZqe40/GEaAdN6ANoCEdArbXpKjBVMnV9lChoBkdAkwpkk8ifQWgHTegDaAhHQK26VjBEa2p1fZQoaAZHQJP6ylEZzgdoB03oA2gIR0Ctu3dld1MedX2UKGgGR0CR5SHiWE9MaAdN6ANoCEdArcIEXpGFz3V9lChoBkdAkSD3enAIp2gHTegDaAhHQK3CDO+qR2d1fZQoaAZHQJSsLJdSl31oB03oA2gIR0CtxlL74zrNdX2UKGgGR0CJfM9PDYRNaAdN6ANoCEdArcdqTY/Vy3V9lChoBkdAjrOU163RX2gHTegDaAhHQK3OQHWSU1R1fZQoaAZHQJINddVvMr5oB03oA2gIR0CtzkjuKGcndX2UKGgGR0CIcCVRDTjOaAdN6ANoCEdArdKq1/lQuXV9lChoBkdAiaS624NI9WgHTegDaAhHQK3T0SXdCVt1fZQoaAZHQIYe6SRr8BNoB03oA2gIR0Ct2noOYplSdX2UKGgGR0CG/EZIg/1QaAdN6ANoCEdArdqCebutwXV9lChoBkdAlQwZl8PWhGgHTegDaAhHQK3e/ub7TDx1fZQoaAZHQJMCwvEjxCpoB03oA2gIR0Ct4Cwb+98JdX2UKGgGR0CVP1FA3T/iaAdN6ANoCEdArebc8/2TPnV9lChoBkdAiqQfReC04WgHTegDaAhHQK3m5ZQHiWF1fZQoaAZHQJXeHVurIYFoB03oA2gIR0Ct60M3Q2MsdX2UKGgGR0CRbaqubI91aAdN6ANoCEdArexgVsUIs3V9lChoBkdAkhLpnL7oCGgHTegDaAhHQK3zKb70nPV1fZQoaAZHQIngfAymALBoB03oA2gIR0Ct8zS08eS0dX2UKGgGR0CSdmNtqHoHaAdN6ANoCEdArfepl6JIlXV9lChoBkdAkVzSHmA9V2gHTegDaAhHQK34zP557gN1fZQoaAZHQJCkt6By0a9oB03oA2gIR0Ct/4oaLn9vdX2UKGgGR0CTCYGM4tHyaAdN6ANoCEdArf+ShJyyU3V9lChoBkdAl0R2NFSbY2gHTegDaAhHQK4D9Drqt5l1fZQoaAZHQJRQ0N9YwItoB03oA2gIR0CuBRIWHk92dX2UKGgGR0CYM17SRbKSaAdN6ANoCEdArgv1PacqfHV9lChoBkdAmUS+maYu02gHTegDaAhHQK4L/gLJCBx1fZQoaAZHQJgqPo9s7+1oB03oA2gIR0CuEHKgh8pkdX2UKGgGR0CZTZBqKxcFaAdN6ANoCEdArhGXCKrJbXV9lChoBkdAkqQ7P6be/GgHTegDaAhHQK4YXn3cpLF1fZQoaAZHQJRpAC2c8T1oB03oA2gIR0CuGGcwpON6dX2UKGgGR0CThOwwCbMHaAdN6ANoCEdArhzxCngpB3V9lChoBkdAlsY9Jz1bq2gHTegDaAhHQK4eGo2GZeB1fZQoaAZHQJT1h7Uoa1loB03oA2gIR0CuJL5zxPO6dX2UKGgGR0CSfGw5eZ5SaAdN6ANoCEdAriTHbh3qzXVlLg=="}, "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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+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 sha256:047ea223bae2a6e2662608d9b3b949849e4a5825d6506bcfd3b6a477972f8a12
+size 1091521

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1296.7569986187386, "std_reward": 221.9186394554428, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-31T18:45:36.762705"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:abc08d8fa8ee039cc42c99ca01e3d7ab8a38a9c8e12454400989d5424b54fea1
+size 2136