Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: SAC
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v3
+      type: PandaReachDense-v3
+    metrics:
+    - type: mean_reward
+      value: -0.18 +/- 0.09
+      name: mean_reward
+      verified: false
+---
+
+# **SAC** Agent playing **PandaReachDense-v3**
+This is a trained model of a **SAC** agent playing **PandaReachDense-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:": "gAWVNwAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnNhYy5wb2xpY2llc5SMEE11bHRpSW5wdXRQb2xpY3mUk5Qu", "__module__": "stable_baselines3.sac.policies", "__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 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 MultiInputPolicy.__init__ at 0x79a10d247380>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x79a10d251900>"}, "verbose": 1, "policy_kwargs": {"use_sde": false}, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1738174846149880627, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 2.0494727e-02 -2.4923915e-02  1.6584507e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [-4.8819251e-02 -4.5552574e-02  2.0798686e-01]\n [ 7.8304656e-02 -2.2134222e-02  1.8818577e-01]]", "desired_goal": "[[-0.13718124 -0.11082271  0.02495823]\n [-0.05346265 -0.06460897  0.15749852]\n [-0.0922733  -0.04464531  0.24275303]\n [ 0.10261903 -0.06568309  0.13476571]]", "observation": "[[ 2.04947274e-02 -2.49239150e-02  1.65845066e-01 -1.03794420e+00\n  -1.22464681e+00 -1.19935310e+00]\n [ 3.84396687e-02 -2.19447225e-12  1.97400138e-01  0.00000000e+00\n  -0.00000000e+00  0.00000000e+00]\n [-4.88192514e-02 -4.55525741e-02  2.07986861e-01 -3.97225857e-01\n  -1.20019756e-01  7.78741241e-01]\n [ 7.83046558e-02 -2.21342221e-02  1.88185766e-01  3.80065352e-01\n  -9.08644497e-01 -1.09551477e+00]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [-5.0941579e-02 -1.0917040e-01  6.6766463e-02]\n [-6.6927848e-03 -2.2814738e-02  1.9223566e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]", "desired_goal": "[[-0.13718124 -0.11082271  0.02495823]\n [-0.08405509 -0.14290383  0.04042742]\n [-0.0922733  -0.04464531  0.24275303]\n [ 0.10261903 -0.06568309  0.13476571]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [-5.0941579e-02 -1.0917040e-01  6.6766463e-02 -8.0511504e-01\n  -6.6713333e-01 -9.5719218e-01]\n [-6.6927848e-03 -2.2814738e-02  1.9223566e-01 -6.4597124e-01\n  -6.5482599e-01  3.3850342e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"}, "_episode_num": 331766, "use_sde": false, "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:": "gAWVhgAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIhlLg=="}, "_n_updates": 249975, "buffer_size": 1000000, "batch_size": 256, "learning_starts": 100, "tau": 0.005, "gamma": 0.99, "gradient_steps": 1, "optimize_memory_usage": false, "replay_buffer_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwQRGljdFJlcGxheUJ1ZmZlcpSTlC4=", "__module__": "stable_baselines3.common.buffers", "__annotations__": "{'observation_space': <class 'gymnasium.spaces.dict.Dict'>, 'obs_shape': dict[str, tuple[int, ...]], 'observations': dict[str, numpy.ndarray], 'next_observations': dict[str, numpy.ndarray]}", "__doc__": "\n    Dict Replay buffer used in off-policy algorithms like SAC/TD3.\n    Extends the ReplayBuffer to use dictionary observations\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        Disabled for now (see https://github.com/DLR-RM/stable-baselines3/pull/243#discussion_r531535702)\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 DictReplayBuffer.__init__ at 0x79a10d19c540>", "add": "<function DictReplayBuffer.add at 0x79a10d19c680>", "sample": "<function DictReplayBuffer.sample at 0x79a10d19c720>", "_get_samples": "<function DictReplayBuffer._get_samples at 0x79a10d19c7c0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x79a10d34c580>"}, "replay_buffer_kwargs": {}, "train_freq": {":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>", ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLAWgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"}, "use_sde_at_warmup": false, "target_entropy": -3.0, "ent_coef": "auto", "target_update_interval": 1, "observation_space": {":type:": "<class 'gymnasium.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "{'achieved_goal': Box(-10.0, 10.0, (3,), float32), 'desired_goal': Box(-10.0, 10.0, (3,), float32), 'observation': Box(-10.0, 10.0, (6,), float32)}", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "bounded_below": "[ True  True  True]", "high": "[1. 1. 1.]", "bounded_above": "[ True  True  True]", "low_repr": "-1.0", "high_repr": "1.0", "_np_random": "Generator(PCG64)"}, "n_envs": 4, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "batch_norm_stats": [], "batch_norm_stats_target": [], "system_info": {"OS": "Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024", "Python": "3.11.11", "Stable-Baselines3": "2.5.0", "PyTorch": "2.5.1+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.1", "Gymnasium": "1.0.0", "OpenAI Gym": "0.25.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -0.17577242013067007, "std_reward": 0.0860859394494428, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-01-29T19:58:30.473140"}

sac-PandaReachDense-v3.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:345629e66f0d3642765fb71354a716a26e1011c885e281e6a4b115ba5c9b6cad
+size 3143188

sac-PandaReachDense-v3/_stable_baselines3_version

@@ -0,0 +1 @@
+2.5.0

sac-PandaReachDense-v3/actor.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b235f0907ecf8a56f75d63e7cef4b2f02309e2d42d2a1c6bbccfd4c647b97ce1
+size 572238

sac-PandaReachDense-v3/critic.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2fb4f37b28a0f4c81b9d891e19dec7d56a35d06cc946becc294713a82665e23e
+size 1132458

sac-PandaReachDense-v3/data

@@ -0,0 +1,114 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVNwAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnNhYy5wb2xpY2llc5SMEE11bHRpSW5wdXRQb2xpY3mUk5Qu",
+        "__module__": "stable_baselines3.sac.policies",
+        "__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 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 MultiInputPolicy.__init__ at 0x79a10d247380>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x79a10d251900>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        "use_sde": false
+    },
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1738174846149880627,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 2.0494727e-02 -2.4923915e-02  1.6584507e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [-4.8819251e-02 -4.5552574e-02  2.0798686e-01]\n [ 7.8304656e-02 -2.2134222e-02  1.8818577e-01]]",
+        "desired_goal": "[[-0.13718124 -0.11082271  0.02495823]\n [-0.05346265 -0.06460897  0.15749852]\n [-0.0922733  -0.04464531  0.24275303]\n [ 0.10261903 -0.06568309  0.13476571]]",
+        "observation": "[[ 2.04947274e-02 -2.49239150e-02  1.65845066e-01 -1.03794420e+00\n  -1.22464681e+00 -1.19935310e+00]\n [ 3.84396687e-02 -2.19447225e-12  1.97400138e-01  0.00000000e+00\n  -0.00000000e+00  0.00000000e+00]\n [-4.88192514e-02 -4.55525741e-02  2.07986861e-01 -3.97225857e-01\n  -1.20019756e-01  7.78741241e-01]\n [ 7.83046558e-02 -2.21342221e-02  1.88185766e-01  3.80065352e-01\n  -9.08644497e-01 -1.09551477e+00]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [-5.0941579e-02 -1.0917040e-01  6.6766463e-02]\n [-6.6927848e-03 -2.2814738e-02  1.9223566e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]",
+        "desired_goal": "[[-0.13718124 -0.11082271  0.02495823]\n [-0.08405509 -0.14290383  0.04042742]\n [-0.0922733  -0.04464531  0.24275303]\n [ 0.10261903 -0.06568309  0.13476571]]",
+        "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [-5.0941579e-02 -1.0917040e-01  6.6766463e-02 -8.0511504e-01\n  -6.6713333e-01 -9.5719218e-01]\n [-6.6927848e-03 -2.2814738e-02  1.9223566e-01 -6.4597124e-01\n  -6.5482599e-01  3.3850342e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"
+    },
+    "_episode_num": 331766,
+    "use_sde": false,
+    "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:": "gAWVhgAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIhlLg=="
+    },
+    "_n_updates": 249975,
+    "buffer_size": 1000000,
+    "batch_size": 256,
+    "learning_starts": 100,
+    "tau": 0.005,
+    "gamma": 0.99,
+    "gradient_steps": 1,
+    "optimize_memory_usage": false,
+    "replay_buffer_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVOQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwQRGljdFJlcGxheUJ1ZmZlcpSTlC4=",
+        "__module__": "stable_baselines3.common.buffers",
+        "__annotations__": "{'observation_space': <class 'gymnasium.spaces.dict.Dict'>, 'obs_shape': dict[str, tuple[int, ...]], 'observations': dict[str, numpy.ndarray], 'next_observations': dict[str, numpy.ndarray]}",
+        "__doc__": "\n    Dict Replay buffer used in off-policy algorithms like SAC/TD3.\n    Extends the ReplayBuffer to use dictionary observations\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        Disabled for now (see https://github.com/DLR-RM/stable-baselines3/pull/243#discussion_r531535702)\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 DictReplayBuffer.__init__ at 0x79a10d19c540>",
+        "add": "<function DictReplayBuffer.add at 0x79a10d19c680>",
+        "sample": "<function DictReplayBuffer.sample at 0x79a10d19c720>",
+        "_get_samples": "<function DictReplayBuffer._get_samples at 0x79a10d19c7c0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x79a10d34c580>"
+    },
+    "replay_buffer_kwargs": {},
+    "train_freq": {
+        ":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>",
+        ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLAWgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"
+    },
+    "use_sde_at_warmup": false,
+    "target_entropy": -3.0,
+    "ent_coef": "auto",
+    "target_update_interval": 1,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.dict.Dict'>",
+        ":serialized:": "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",
+        "spaces": "{'achieved_goal': Box(-10.0, 10.0, (3,), float32), 'desired_goal': Box(-10.0, 10.0, (3,), float32), 'observation': Box(-10.0, 10.0, (6,), float32)}",
+        "_shape": null,
+        "dtype": null,
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            3
+        ],
+        "low": "[-1. -1. -1.]",
+        "bounded_below": "[ True  True  True]",
+        "high": "[1. 1. 1.]",
+        "bounded_above": "[ True  True  True]",
+        "low_repr": "-1.0",
+        "high_repr": "1.0",
+        "_np_random": "Generator(PCG64)"
+    },
+    "n_envs": 4,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "batch_norm_stats": [],
+    "batch_norm_stats_target": []
+}

sac-PandaReachDense-v3/ent_coef_optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:294e0283273d4ce46d97d81f7226182d71337965ce2fc5f589909618faa7c4d3
+size 1940

sac-PandaReachDense-v3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:db6609cfadd2e9f742f292ede4b930bac3567b44bde0659b4e264c0ad355e575
+size 1417078

sac-PandaReachDense-v3/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e412976d4c71a9d38074f416a1defc467e61d33dba41dcea3bd2f2d5844b732f
+size 1180

sac-PandaReachDense-v3/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024
+- Python: 3.11.11
+- Stable-Baselines3: 2.5.0
+- PyTorch: 2.5.1+cu121
+- GPU Enabled: True
+- Numpy: 1.26.4
+- Cloudpickle: 3.1.1
+- Gymnasium: 1.0.0
+- OpenAI Gym: 0.25.2

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:61521366defdbdcd58a3086fc3b9ed883f8b89265e6c873c0839e4d2d92a0379
+size 2826