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: 1735.79 +/- 115.00
+      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:c4e171e1504f9ee4b76cb01b521d09fe2df29d4247bbf67b25591495ce0e5fe8
+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 0x7fad819dd310>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fad819dd3a0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fad819dd430>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fad819dd4c0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fad819dd550>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fad819dd5e0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fad819dd670>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fad819dd700>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fad819dd790>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fad819dd820>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fad819dd8b0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fad819dd940>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fad819d6720>"
+    },
+    "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": 1677695421693902321,
+    "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:a2c830be9f321518923a8a878c3b8d7dffc8af595f4fd92a0247e4098bdd504e
+size 56190

a2c-AntBulletEnv-v0/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e9d15882ff5cde940df1fe07e19e93c1ba4d1f83aa42597f1adc74458dc7c1d9
+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: 1.22.4
+- 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 0x7fad819dd310>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fad819dd3a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fad819dd430>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fad819dd4c0>", "_build": "<function ActorCriticPolicy._build at 0x7fad819dd550>", "forward": "<function ActorCriticPolicy.forward at 0x7fad819dd5e0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fad819dd670>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fad819dd700>", "_predict": "<function ActorCriticPolicy._predict at 0x7fad819dd790>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fad819dd820>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fad819dd8b0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fad819dd940>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fad819d6720>"}, "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": 1677695421693902321, "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:": "gAWVRAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQJIwLhcZ9/mMAWyUTegDjAF0lEdAruqkKzAvc3V9lChoBkdAlvRvsu3+dmgHTegDaAhHQK7s3QHiWE91fZQoaAZHQJYUSR4hUzdoB03oA2gIR0Cu8Of2TPjXdX2UKGgGR0CS5vaTfR/maAdN6ANoCEdArvg8snRb8nV9lChoBkdAj7ifrjYI0WgHTegDaAhHQK74wnYxtYV1fZQoaAZHQJMIb5eqrBFoB03oA2gIR0Cu+5Z5AyEddX2UKGgGR0CUp06Ae7tiaAdN6ANoCEdArwGp6QeV9nV9lChoBkdAj4JprULDymgHTegDaAhHQK8KCq4pc5d1fZQoaAZHQJY4Zi+cpb5oB03oA2gIR0CvCo0r9VFQdX2UKGgGR0CX8h75mAbyaAdN6ANoCEdArwy2ECeVcHV9lChoBkdAk5Yiojv/i2gHTegDaAhHQK8QpfdAPd51fZQoaAZHQJKnti/fwZxoB03oA2gIR0CvF/p5mh/RdX2UKGgGR0CWYyZGKAJ+aAdN6ANoCEdArxiRPKuB+XV9lChoBkdAlrRd5prULGgHTegDaAhHQK8buULUkOZ1fZQoaAZHQJYkTGR3eN1oB03oA2gIR0CvIdaBI4EPdX2UKGgGR0CThoahYeT3aAdN6ANoCEdArylZYcNpd3V9lChoBkdAlqPsfaHsTmgHTegDaAhHQK8p4AbyYol1fZQoaAZHQJf3CizsyBVoB03oA2gIR0CvLAa2v0ROdX2UKGgGR0CV9FuxrzoVaAdN6ANoCEdAry/9DhLoOnV9lChoBkdAlb01ImPYF2gHTegDaAhHQK83nb8m8dx1fZQoaAZHQJXmsAKfFrFoB03oA2gIR0CvOGD1GsmwdX2UKGgGR0CZAnADaGpNaAdN6ANoCEdArzuhEfDDTHV9lChoBkdAk1EhXXAdn2gHTegDaAhHQK9Bfm+0w8J1fZQoaAZHQJbVumbb1yxoB03oA2gIR0CvSND5KvmpdX2UKGgGR0CUZq/Q0GeMaAdN6ANoCEdAr0lY4ffXPXV9lChoBkdAljyLThHby2gHTegDaAhHQK9LjCXyAhB1fZQoaAZHQJb973h4t6JoB03oA2gIR0CvT3ueJ53UdX2UKGgGR0CVEf/hESdwaAdN6ANoCEdAr1eY31jAi3V9lChoBkdAln2LXcxj8WgHTegDaAhHQK9YabaRISV1fZQoaAZHQJR5W6PKdQRoB03oA2gIR0CvXA2JaaCudX2UKGgGR0CUJRS8an76aAdN6ANoCEdAr2GEscyWRnV9lChoBkdAk5CakAPuomgHTegDaAhHQK9o28RL9Mt1fZQoaAZHQJfL+O7xusNoB03oA2gIR0CvaWwi7kGSdX2UKGgGR0CUGl95yEL6aAdN6ANoCEdAr2ukyad+X3V9lChoBkdAl815KzzErGgHTegDaAhHQK9vkQ5FPSF1fZQoaAZHQJbNp/XoTwloB03oA2gIR0CveE8SwnpjdX2UKGgGR0CUsCposZpBaAdN6ANoCEdAr3kiIDYAbXV9lChoBkdAlvxwJw84gmgHTegDaAhHQK98iW8AaNx1fZQoaAZHQJSWLdHlOoJoB03oA2gIR0CvgVJyp71JdX2UKGgGR0CXcm/IbOu8aAdN6ANoCEdAr4klRtP56HV9lChoBkdAlk/C5AhStWgHTegDaAhHQK+Jt17pmmN1fZQoaAZHQJR1KCg9NetoB03oA2gIR0CvjCJ7b+LndX2UKGgGR0CWV5NgBtDVaAdN6ANoCEdAr5CMXP7emHV9lChoBkdAl/+5Ec81XWgHTegDaAhHQK+b5TgEU0x1fZQoaAZHQJdORCWu5jJoB03oA2gIR0CvnM2I42jxdX2UKGgGR0CPM4pGWldkaAdN6ANoCEdAr59lIEr5I3V9lChoBkdAmP6XrY5DJGgHTegDaAhHQK+jpsCT2WZ1fZQoaAZHQJSg0ZR8+idoB03oA2gIR0Cvq9N6w+t9dX2UKGgGR0CaaN84PwuvaAdN6ANoCEdAr6xnh/Aj6nV9lChoBkdAl4mvJiiItWgHTegDaAhHQK+u4SRr8BN1fZQoaAZHQJq5ySbH6uZoB03oA2gIR0CvtCnV5KODdX2UKGgGR0CbpnkgfU4JaAdN6ANoCEdAr773tKIznHV9lChoBkdAkk3wiiZfD2gHTegDaAhHQK+/i49X9zh1fZQoaAZHQJtY718LKFJoB03oA2gIR0CvwfsyJsO5dX2UKGgGR0CXnWpo9LYgaAdN6ANoCEdAr8ZkRpUPx3V9lChoBkdAlWHyZv1lG2gHTegDaAhHQK/O9aDf3vh1fZQoaAZHQJS1lR4yGi5oB03oA2gIR0Cvz5HyEtdzdX2UKGgGR0CY06Btk4FSaAdN6ANoCEdAr9LkJ2MbWHV9lChoBkdAlK7F+uvECWgHTegDaAhHQK/Z7+yZ8a51fZQoaAZHQJaCEWSEDhdoB03oA2gIR0Cv4y1QhwERdX2UKGgGR0CVXgDQZ4wAaAdN6ANoCEdAr+PHQBxPwnV9lChoBkdAm4FrTQVsUWgHTegDaAhHQK/mPos7MgV1fZQoaAZHQJw2hdX1antoB03oA2gIR0Cv6rks8PnTdX2UKGgGR0CYPgpsXSBtaAdN6ANoCEdAr/SExwhnrnV9lChoBkdAkpUNpmEoOWgHTegDaAhHQK/1dR4yGi51fZQoaAZHQJvL1gmZ3LVoB03oA2gIR0Cv+X0ahpQDdX2UKGgGR0CUB8AH3UQTaAdN6ANoCEdAr/8f+ZPVNHV9lChoBkdAl9bGgJ1JUmgHTegDaAhHQLAD8eenQ6Z1fZQoaAZHQJXfY+PikwhoB03oA2gIR0CwBEC/j81odX2UKGgGR0CRz76MR6F/aAdN6ANoCEdAsAWKPeYUnHV9lChoBkdAnA9sKkVN6GgHTegDaAhHQLAH5t2s7uF1fZQoaAZHQJM5uU4aP0ZoB03oA2gIR0CwDhOERJ2/dX2UKGgGR0CVLTL5ylvZaAdN6ANoCEdAsA5gEr5IpnV9lChoBkdAm/qetCAtnWgHTegDaAhHQLAPnm5Dqnp1fZQoaAZHQJ1yeBDohZBoB03oA2gIR0CwEd3OGCZndX2UKGgGR0CVNShRqGlAaAdN6ANoCEdAsBYonDziCXV9lChoBkdAm9LeWfK6nWgHTegDaAhHQLAWdNyo4uN1fZQoaAZHQJ0QyROk+HJoB03oA2gIR0CwF9gfp2U0dX2UKGgGR0CbguRLsa86aAdN6ANoCEdAsBtUWhysCHV9lChoBkdAkY5np4bCJ2gHTegDaAhHQLAgW81Gb1B1fZQoaAZHQJrnPz6JqItoB03oA2gIR0CwIKp71Iy1dX2UKGgGR0CTIHlsP8Q7aAdN6ANoCEdAsCHuqT8pC3V9lChoBkdAlnAqS1Vo6GgHTegDaAhHQLAkMphnanJ1fZQoaAZHQJkCERK6FuhoB03oA2gIR0CwKRmhRIjGdX2UKGgGR0CIHeU4aP0aaAdN6ANoCEdAsCmUE2YOUnV9lChoBkdAkTgSBbwBo2gHTegDaAhHQLArocdYGMZ1fZQoaAZHQJCtYyckMThoB03oA2gIR0CwLq8hkiD/dX2UKGgGR0CWBmPUaybAaAdN6ANoCEdAsDL2NwR5DHV9lChoBkdAlw6Cpm29c2gHTegDaAhHQLAzQRMN+b51fZQoaAZHQJORampEQXhoB03oA2gIR0CwNHRs67uldX2UKGgGR0CWmGbtJFspaAdN6ANoCEdAsDavI4lyBHV9lChoBkdAlDZzTfBN22gHTegDaAhHQLA82UQTVUd1fZQoaAZHQJJFYDbJwKloB03oA2gIR0CwPVVPN3W4dX2UKGgGR0CVlEDHOryUaAdN6ANoCEdAsD66uNgjQnV9lChoBkdAlmz5iZv1lGgHTegDaAhHQLBBIACnxax1fZQoaAZHQJf4hcC5mRNoB03oA2gIR0CwRXxiG34LdX2UKGgGR0CaE8cbBGhFaAdN6ANoCEdAsEXLCQ9zO3V9lChoBkdAmiyPGQ0XQGgHTegDaAhHQLBHHJV81Gd1fZQoaAZHQJeYWW+oLohoB03oA2gIR0CwSiCed07sdX2UKGgGR0CShtx1xKg7aAdN6ANoCEdAsE9ny3CsO3VlLg=="}, "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": "1.22.4", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:93fbd64f2f4c5fb363fc12479d276d27df870ce2b5b8c8f0f6ace37f1c837fcc
+size 1054021

results.json

@@ -0,0 +1 @@
+{"mean_reward": 1735.7871385008098, "std_reward": 115.0020683393256, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-01T19:40:07.527924"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c33d608ac96b2b48229161cc910cd2194ec665467613339813584d33abafca9
+size 2136