Upload sac BipedalWalker-v3 trained agent

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- BipedalWalker-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: sac
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: BipedalWalker-v3
+      type: BipedalWalker-v3
+    metrics:
+    - type: mean_reward
+      value: -99.46 +/- 46.60
+      name: mean_reward
+      verified: false
+---
+
+# **sac** Agent playing **BipedalWalker-v3**
+This is a trained model of a **sac** agent playing **BipedalWalker-v3**
+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
+
+...
+```

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVMAAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnNhYy5wb2xpY2llc5SMCVNBQ1BvbGljeZSTlC4=", "__module__": "stable_baselines3.sac.policies", "__annotations__": "{'actor': <class 'stable_baselines3.sac.policies.Actor'>, 'critic': <class 'stable_baselines3.common.policies.ContinuousCritic'>, 'critic_target': <class 'stable_baselines3.common.policies.ContinuousCritic'>}", "__doc__": "\n    Policy class (with both actor and critic) for SAC.\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 use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE 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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\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    :param n_critics: Number of critic networks to create.\n    :param share_features_extractor: Whether to share or not the features extractor\n        between the actor and the critic (this saves computation time)\n    ", "__init__": "<function SACPolicy.__init__ at 0x7964a088fc70>", "_build": "<function SACPolicy._build at 0x7964a088fd00>", "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7964a088fd90>", "reset_noise": "<function SACPolicy.reset_noise at 0x7964a088fe20>", "make_actor": "<function SACPolicy.make_actor at 0x7964a088feb0>", "make_critic": "<function SACPolicy.make_critic at 0x7964a088ff40>", "forward": "<function SACPolicy.forward at 0x7964a08b4040>", "_predict": "<function SACPolicy._predict at 0x7964a08b40d0>", "set_training_mode": "<function SACPolicy.set_training_mode at 0x7964a08b4160>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7964a08ae640>"}, "verbose": 5, "policy_kwargs": {"log_std_init": -3, "net_arch": [400, 300], "use_sde": true}, "num_timesteps": 500736, "_total_timesteps": 500000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1704045945719916661, "learning_rate": 0.00073, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAEBAQEBAQEBAQEBAQEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_episode_num": 643, "use_sde": true, "sde_sample_freq": -1, "_current_progress_remaining": -0.0014719999999999178, "_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": 30720, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True  True  True  True  True\n  True  True  True  True  True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True  True  True  True  True\n  True  True  True  True  True  True  True  True  True  True  True  True]", "_shape": [24], "low": "[-3.1415927 -5.        -5.        -5.        -3.1415927 -5.\n -3.1415927 -5.        -0.        -3.1415927 -5.        -3.1415927\n -5.        -0.        -1.        -1.        -1.        -1.\n -1.        -1.        -1.        -1.        -1.        -1.       ]", "high": "[3.1415927 5.        5.        5.        3.1415927 5.        3.1415927\n 5.        5.        3.1415927 5.        3.1415927 5.        5.\n 1.        1.        1.        1.        1.        1.        1.\n 1.        1.        1.       ]", "low_repr": "[-3.1415927 -5.        -5.        -5.        -3.1415927 -5.\n -3.1415927 -5.        -0.        -3.1415927 -5.        -3.1415927\n -5.        -0.        -1.        -1.        -1.        -1.\n -1.        -1.        -1.        -1.        -1.        -1.       ]", "high_repr": "[3.1415927 5.        5.        5.        3.1415927 5.        3.1415927\n 5.        5.        3.1415927 5.        3.1415927 5.        5.\n 1.        1.        1.        1.        1.        1.        1.\n 1.        1.        1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True  True  True  True]", "bounded_above": "[ True  True  True  True]", "_shape": [4], "low": "[-1. -1. -1. -1.]", "high": "[1. 1. 1. 1.]", "low_repr": "-1.0", "high_repr": "1.0", "_np_random": "Generator(PCG64)"}, "n_envs": 16, "buffer_size": 300000, "batch_size": 256, "learning_starts": 10000, "tau": 0.02, "gamma": 0.98, "gradient_steps": 64, "optimize_memory_usage": false, "replay_buffer_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVNQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwMUmVwbGF5QnVmZmVylJOULg==", "__module__": "stable_baselines3.common.buffers", "__annotations__": "{'observations': <class 'numpy.ndarray'>, 'next_observations': <class 'numpy.ndarray'>, 'actions': <class 'numpy.ndarray'>, 'rewards': <class 'numpy.ndarray'>, 'dones': <class 'numpy.ndarray'>, 'timeouts': <class 'numpy.ndarray'>}", "__doc__": "\n    Replay buffer used in off-policy algorithms like SAC/TD3.\n\n    :param buffer_size: Max number of element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param device: PyTorch device\n    :param n_envs: Number of parallel environments\n    :param optimize_memory_usage: Enable a memory efficient variant\n        of the replay buffer which reduces by almost a factor two the memory used,\n        at a cost of more complexity.\n        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n        and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n        Cannot be used in combination with handle_timeout_termination.\n    :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n        separately and treat the task as infinite horizon task.\n        https://github.com/DLR-RM/stable-baselines3/issues/284\n    ", "__init__": "<function ReplayBuffer.__init__ at 0x7964a09a3d00>", "add": "<function ReplayBuffer.add at 0x7964a09a3d90>", "sample": "<function ReplayBuffer.sample at 0x7964a09a3e20>", "_get_samples": "<function ReplayBuffer._get_samples at 0x7964a09a3eb0>", "_maybe_cast_dtype": "<staticmethod(<function ReplayBuffer._maybe_cast_dtype at 0x7964a09a3f40>)>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7964a09b3280>"}, "replay_buffer_kwargs": {}, "train_freq": {":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>", ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLQGgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"}, "use_sde_at_warmup": false, "target_entropy": -4.0, "ent_coef": "auto", "target_update_interval": 1, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "batch_norm_stats": [], "batch_norm_stats_target": [], "system_info": {"OS": "Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.2.1", "PyTorch": "2.1.0+cu121", "GPU Enabled": "False", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

logs/events.out.tfevents.1704045945.7f224148deca.381.0

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5808470206fb26c935db76273d8997ae0fe9fd472541b7290f13b1df8a47ebdd
+size 441

results.json

@@ -0,0 +1 @@
+{"mean_reward": -99.46183679999999, "std_reward": 46.602285853256525, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-12-31T18:42:32.813851"}

sac-BipedalWalker-v3.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b008d1befb5d9773982e8989699e6e99e0435a4b996d39c0b428afed64cbf38d
+size 5873841

sac-BipedalWalker-v3/_stable_baselines3_version

@@ -0,0 +1 @@
+2.2.1

sac-BipedalWalker-v3/actor.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0f56c47ad53f7c205d11cff3c2ba10eca3ebb0d1a8863850061924cfa57f4c33
+size 1067607

sac-BipedalWalker-v3/critic.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4df37e8a12830adbd79f3250206d8971f8f62923345031994be211fe3c2d601d
+size 2124522

sac-BipedalWalker-v3/data

@@ -0,0 +1,130 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVMAAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnNhYy5wb2xpY2llc5SMCVNBQ1BvbGljeZSTlC4=",
+        "__module__": "stable_baselines3.sac.policies",
+        "__annotations__": "{'actor': <class 'stable_baselines3.sac.policies.Actor'>, 'critic': <class 'stable_baselines3.common.policies.ContinuousCritic'>, 'critic_target': <class 'stable_baselines3.common.policies.ContinuousCritic'>}",
+        "__doc__": "\n    Policy class (with both actor and critic) for SAC.\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 use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE 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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\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    :param n_critics: Number of critic networks to create.\n    :param share_features_extractor: Whether to share or not the features extractor\n        between the actor and the critic (this saves computation time)\n    ",
+        "__init__": "<function SACPolicy.__init__ at 0x7964a088fc70>",
+        "_build": "<function SACPolicy._build at 0x7964a088fd00>",
+        "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7964a088fd90>",
+        "reset_noise": "<function SACPolicy.reset_noise at 0x7964a088fe20>",
+        "make_actor": "<function SACPolicy.make_actor at 0x7964a088feb0>",
+        "make_critic": "<function SACPolicy.make_critic at 0x7964a088ff40>",
+        "forward": "<function SACPolicy.forward at 0x7964a08b4040>",
+        "_predict": "<function SACPolicy._predict at 0x7964a08b40d0>",
+        "set_training_mode": "<function SACPolicy.set_training_mode at 0x7964a08b4160>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7964a08ae640>"
+    },
+    "verbose": 5,
+    "policy_kwargs": {
+        "log_std_init": -3,
+        "net_arch": [
+            400,
+            300
+        ],
+        "use_sde": true
+    },
+    "num_timesteps": 500736,
+    "_total_timesteps": 500000.0,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1704045945719916661,
+    "learning_rate": 0.00073,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAEBAQEBAQEBAQEBAQEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_episode_num": 643,
+    "use_sde": true,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.0014719999999999178,
+    "_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": 30720,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True  True  True  True  True  True  True  True  True\n  True  True  True  True  True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True  True  True  True  True\n  True  True  True  True  True  True  True  True  True  True  True  True]",
+        "_shape": [
+            24
+        ],
+        "low": "[-3.1415927 -5.        -5.        -5.        -3.1415927 -5.\n -3.1415927 -5.        -0.        -3.1415927 -5.        -3.1415927\n -5.        -0.        -1.        -1.        -1.        -1.\n -1.        -1.        -1.        -1.        -1.        -1.       ]",
+        "high": "[3.1415927 5.        5.        5.        3.1415927 5.        3.1415927\n 5.        5.        3.1415927 5.        3.1415927 5.        5.\n 1.        1.        1.        1.        1.        1.        1.\n 1.        1.        1.       ]",
+        "low_repr": "[-3.1415927 -5.        -5.        -5.        -3.1415927 -5.\n -3.1415927 -5.        -0.        -3.1415927 -5.        -3.1415927\n -5.        -0.        -1.        -1.        -1.        -1.\n -1.        -1.        -1.        -1.        -1.        -1.       ]",
+        "high_repr": "[3.1415927 5.        5.        5.        3.1415927 5.        3.1415927\n 5.        5.        3.1415927 5.        3.1415927 5.        5.\n 1.        1.        1.        1.        1.        1.        1.\n 1.        1.        1.       ]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True]",
+        "bounded_above": "[ True  True  True  True]",
+        "_shape": [
+            4
+        ],
+        "low": "[-1. -1. -1. -1.]",
+        "high": "[1. 1. 1. 1.]",
+        "low_repr": "-1.0",
+        "high_repr": "1.0",
+        "_np_random": "Generator(PCG64)"
+    },
+    "n_envs": 16,
+    "buffer_size": 300000,
+    "batch_size": 256,
+    "learning_starts": 10000,
+    "tau": 0.02,
+    "gamma": 0.98,
+    "gradient_steps": 64,
+    "optimize_memory_usage": false,
+    "replay_buffer_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVNQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwMUmVwbGF5QnVmZmVylJOULg==",
+        "__module__": "stable_baselines3.common.buffers",
+        "__annotations__": "{'observations': <class 'numpy.ndarray'>, 'next_observations': <class 'numpy.ndarray'>, 'actions': <class 'numpy.ndarray'>, 'rewards': <class 'numpy.ndarray'>, 'dones': <class 'numpy.ndarray'>, 'timeouts': <class 'numpy.ndarray'>}",
+        "__doc__": "\n    Replay buffer used in off-policy algorithms like SAC/TD3.\n\n    :param buffer_size: Max number of element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param device: PyTorch device\n    :param n_envs: Number of parallel environments\n    :param optimize_memory_usage: Enable a memory efficient variant\n        of the replay buffer which reduces by almost a factor two the memory used,\n        at a cost of more complexity.\n        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n        and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n        Cannot be used in combination with handle_timeout_termination.\n    :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n        separately and treat the task as infinite horizon task.\n        https://github.com/DLR-RM/stable-baselines3/issues/284\n    ",
+        "__init__": "<function ReplayBuffer.__init__ at 0x7964a09a3d00>",
+        "add": "<function ReplayBuffer.add at 0x7964a09a3d90>",
+        "sample": "<function ReplayBuffer.sample at 0x7964a09a3e20>",
+        "_get_samples": "<function ReplayBuffer._get_samples at 0x7964a09a3eb0>",
+        "_maybe_cast_dtype": "<staticmethod(<function ReplayBuffer._maybe_cast_dtype at 0x7964a09a3f40>)>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7964a09b3280>"
+    },
+    "replay_buffer_kwargs": {},
+    "train_freq": {
+        ":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>",
+        ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLQGgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"
+    },
+    "use_sde_at_warmup": false,
+    "target_entropy": -4.0,
+    "ent_coef": "auto",
+    "target_update_interval": 1,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "batch_norm_stats": [],
+    "batch_norm_stats_target": []
+}

sac-BipedalWalker-v3/ent_coef_optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76e5c62689cd0f285ee0fd26a2870541ddd08258db0c50e63216c88447c773b5
+size 1940

sac-BipedalWalker-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dc60b0553ca0ae488948c9b8064f4ee18a9def4cc0ae2c8af74ade64a22a7ea4
+size 2656313

sac-BipedalWalker-v3/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:936ca3d86e3e5182a363a5322c2ca0c1325288f169824a8e7afe56b5e6800645
+size 1180

sac-BipedalWalker-v3/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.2.1
+- PyTorch: 2.1.0+cu121
+- GPU Enabled: False
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:769741094ceffa237f1550917f9ca3e852a36319e9bb46cf8ccbad427b7fde7e
+size 2911