Upload model to Hugging Face

.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

BC-no-training-far.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ce39735d253e288afeb72a7f24deefece97e95a6e47b477fdb69e1377582a987
+size 44017

BC-no-training-far/_stable_baselines3_version

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

BC-no-training-far/data

@@ -0,0 +1,95 @@
+{
+    "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 0x7f67fe0f12d0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f67fe0f1360>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f67fe0f13f0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f67fe0f1480>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f67fe0f1510>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f67fe0f15a0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f67fe0f1630>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f67fe0f16c0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f67fe0f1750>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f67fe0f17e0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f67fe0f1870>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f67fe0f1900>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f67fe9fe2c0>"
+    },
+    "verbose": true,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            10
+        ],
+        "low": "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]",
+        "high": "[5.2000000e+05 3.1415927e+00 1.0000000e+02 1.0000000e+02 1.0000000e+02\n 1.0000000e+02 1.0000000e+02 1.0000000e+02 1.0000000e+02 1.0000000e+02]",
+        "bounded_below": "[ True  True  True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True  True  True]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "num_timesteps": 32768,
+    "_total_timesteps": 30000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1681940958817542924,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVFQEAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJagAAAAAAAAAGOOhkOgk7+/sb6ZQgAAyEIAAMhCAADIQgAAyEIAAMhCAADIQgAAyEJnxDZDTECtPgAAyEIAAMhCAADIQrNhg0EAAMhCpNVqQgAAyEIAAMhCIblMQ5j5ID8AAMhCdOhvQbajrkG0va9CAADIQvyFUEIAAMhCAADIQgtbHENJcm++KZGyQq0dqEIAAMhCAADIQo4SJEIAAMhCAADIQgAAyEKUjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwRLCoaUjAFDlHSUUpQu"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.09226666666666672,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 40,
+    "n_steps": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

BC-no-training-far/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e416b25ae2a1198ea5f2da063a59e675dfa76992e380bc48b4c3ccf0b7f90ab7
+size 18973

BC-no-training-far/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0ac78df463394a63283d874e0c8f2fe078db30e417892637169941f96f90ae8f
+size 9295

BC-no-training-far/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

BC-no-training-far/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.19.0-35-generic-x86_64-with-glibc2.35 # 36~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Fri Feb 17 15:17:25 UTC 2
+- Python: 3.10.9
+- Stable-Baselines3: 1.7.0
+- PyTorch: 2.0.0
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Gym: 0.21.0

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- RoombaAToB-no-training-far
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: BC
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: RoombaAToB-no-training-far
+      type: RoombaAToB-no-training-far
+    metrics:
+    - type: mean_reward
+      value: -97.11 +/- 0.00
+      name: mean_reward
+      verified: false
+---
+
+# **BC** Agent playing **RoombaAToB-no-training-far**
+This is a trained model of a **BC** agent playing **RoombaAToB-no-training-far**
+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:": "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 0x7f67fe0f12d0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f67fe0f1360>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f67fe0f13f0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f67fe0f1480>", "_build": "<function ActorCriticPolicy._build at 0x7f67fe0f1510>", "forward": "<function ActorCriticPolicy.forward at 0x7f67fe0f15a0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f67fe0f1630>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f67fe0f16c0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f67fe0f1750>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f67fe0f17e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f67fe0f1870>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f67fe0f1900>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f67fe9fe2c0>"}, "verbose": true, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gAWVswEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCoWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWKAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlGgKSwqFlIwBQ5R0lFKUjARoaWdolGgSKJYoAAAAAAAAAADo/UjbD0lAAADIQgAAyEIAAMhCAADIQgAAyEIAAMhCAADIQgAAyEKUaApLCoWUaBV0lFKUjA1ib3VuZGVkX2JlbG93lGgSKJYKAAAAAAAAAAEBAQEBAQEBAQGUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCoWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYKAAAAAAAAAAEBAQEBAQEBAQGUaCFLCoWUaBV0lFKUjApfbnBfcmFuZG9tlE51Yi4=", "dtype": "float32", "_shape": [10], "low": "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]", "high": "[5.2000000e+05 3.1415927e+00 1.0000000e+02 1.0000000e+02 1.0000000e+02\n 1.0000000e+02 1.0000000e+02 1.0000000e+02 1.0000000e+02 1.0000000e+02]", "bounded_below": "[ True  True  True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True  True  True]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 4, "num_timesteps": 32768, "_total_timesteps": 30000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1681940958817542924, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVFQEAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJagAAAAAAAAAGOOhkOgk7+/sb6ZQgAAyEIAAMhCAADIQgAAyEIAAMhCAADIQgAAyEJnxDZDTECtPgAAyEIAAMhCAADIQrNhg0EAAMhCpNVqQgAAyEIAAMhCIblMQ5j5ID8AAMhCdOhvQbajrkG0va9CAADIQvyFUEIAAMhCAADIQgtbHENJcm++KZGyQq0dqEIAAMhCAADIQo4SJEIAAMhCAADIQgAAyEKUjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwRLCoaUjAFDlHSUUpQu"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.09226666666666672, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 40, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.19.0-35-generic-x86_64-with-glibc2.35 # 36~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Fri Feb 17 15:17:25 UTC 2", "Python": "3.10.9", "Stable-Baselines3": "1.7.0", "PyTorch": "2.0.0", "GPU Enabled": "True", "Numpy": "1.23.5", "Gym": "0.21.0"}}

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:825a05450519358052faac2b54f027deabc881e23d06bc7cec758a7db8128639
+size 1281699

results.json

@@ -0,0 +1 @@
+{"mean_reward": -97.11484470367438, "std_reward": 0.0, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-19T14:51:48.955914"}