Upload model to Hugging Face

PPO-mid-goal.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:71be535099d9805f6ee49a323b765ed025ad0e02899dcd607250fad25d01e121
+size 150326

PPO-mid-goal/_stable_baselines3_version

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

PPO-mid-goal/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 0x7fa1650f11b0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa1650f1240>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa1650f12d0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa1650f1360>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fa1650f13f0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fa1650f1480>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa1650f1510>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa1650f15a0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fa1650f1630>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa1650f16c0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa1650f1750>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa1650f17e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fa165636840>"
+    },
+    "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:": "gAWViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 4,
+    "num_timesteps": 57344,
+    "_total_timesteps": 50000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1681942649975070314,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVFQEAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJagAAAAAAAAABa4fUKzqIc/AADIQgAAyEIlhTdCtwJkQgAAyEIAAMhCpu6QQQAAyELZyhJCcKGwPoGpmkIAAMhCAADIQgAAyEIAAMhCAADIQgAAyEKVlp5CY6Z2QmmItz8AAMhCAADIQgAAyEJRYopCAADIQsvvw0EAAMhCAADIQgJlHEJ6JA7AAADIQgAAyEJwAn5C9Ju+QgAAyEIAAMhCJwO/QgAAyEKUjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////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": -0.1468799999999999,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 690,
+    "n_steps": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.5,
+    "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
+}

PPO-mid-goal/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f4462be7bc83eaf11517118e0b8fe1627b1f8c07877ef437781b582a3c8cf32d
+size 90105

PPO-mid-goal/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:14dfda086e482e0bc1afdba17b5aa0161241a35fd6fa11f3857399e8e75dc417
+size 44417

PPO-mid-goal/pytorch_variables.pth

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

PPO-mid-goal/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-mid-goal
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: RoombaAToB-mid-goal
+      type: RoombaAToB-mid-goal
+    metrics:
+    - type: mean_reward
+      value: -113.64 +/- 0.00
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **RoombaAToB-mid-goal**
+This is a trained model of a **PPO** agent playing **RoombaAToB-mid-goal**
+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 0x7fa1650f11b0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa1650f1240>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa1650f12d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa1650f1360>", "_build": "<function ActorCriticPolicy._build at 0x7fa1650f13f0>", "forward": "<function ActorCriticPolicy.forward at 0x7fa1650f1480>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa1650f1510>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa1650f15a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa1650f1630>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa1650f16c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa1650f1750>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa1650f17e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fa165636840>"}, "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:": "gAWViAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 4, "num_timesteps": 57344, "_total_timesteps": 50000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1681942649975070314, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVFQEAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJagAAAAAAAAABa4fUKzqIc/AADIQgAAyEIlhTdCtwJkQgAAyEIAAMhCpu6QQQAAyELZyhJCcKGwPoGpmkIAAMhCAADIQgAAyEIAAMhCAADIQgAAyEKVlp5CY6Z2QmmItz8AAMhCAADIQgAAyEJRYopCAADIQsvvw0EAAMhCAADIQgJlHEJ6JA7AAADIQgAAyEJwAn5C9Ju+QgAAyEIAAMhCJwO/QgAAyEKUjAVudW1weZSMBWR0eXBllJOUjAJmNJSJiIeUUpQoSwOMATyUTk5OSv////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": -0.1468799999999999, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 690, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.5, "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"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -113.64088829040483, "std_reward": 1.4210854715202004e-14, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-19T15:21:35.278806"}