Upload model to Hugging Face

PPO-hardcoded.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:766628f7038ab8c5412453903a3fad2cb95cc81007abb826f84cc9972d527513
+size 142417

PPO-hardcoded/_stable_baselines3_version

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

PPO-hardcoded/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 0x7fa33ffe4e50>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa33ffe4ee0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa33ffe4f70>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa33ffe5000>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fa33ffe5090>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fa33ffe5120>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa33ffe51b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa33ffe5240>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fa33ffe52d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa33ffe5360>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa33ffe53f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa33ffe5480>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fa340b6cdc0>"
+    },
+    "verbose": true,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            5
+        ],
+        "low": "[0. 0. 0. 0. 0.]",
+        "high": "[5.2000000e+05 3.1415927e+00 1.0000000e+02 1.0000000e+02 1.0000000e+02]",
+        "bounded_below": "[ True  True  True  True  True]",
+        "bounded_above": "[ 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": 204800,
+    "_total_timesteps": 200000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1681097712738902504,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVxQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJZQAAAAAAAAACbXp0IBgcy/AADIQgAAyEIAAMhC8xRCQ4tMhT8AAMhCAADIQgAAyEKHZztCUIUAwAAAyEIAAMhCAADIQsDyOUMCtUg/AADIQgAAyEIAAMhClIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwWGlIwBQ5R0lFKULg=="
+    },
+    "_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": -0.02400000000000002,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 250,
+    "n_steps": 2048,
+    "gamma": 0.95,
+    "gae_lambda": 0.8,
+    "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-hardcoded/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f4bef407a9fb5d109db4f526248671b3a334680129b9477019fbb8c9922c68fb
+size 84985

PPO-hardcoded/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8a894cb449b20178d1d91c1eb7151e55192edcfa4e9c70144d1b2e37f98d7f32
+size 41857

PPO-hardcoded/pytorch_variables.pth

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

PPO-hardcoded/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-Hardcoded
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: RoombaAToB-Hardcoded
+      type: RoombaAToB-Hardcoded
+    metrics:
+    - type: mean_reward
+      value: 63.99 +/- 0.00
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **RoombaAToB-Hardcoded**
+This is a trained model of a **PPO** agent playing **RoombaAToB-Hardcoded**
+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 0x7fa33ffe4e50>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa33ffe4ee0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa33ffe4f70>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa33ffe5000>", "_build": "<function ActorCriticPolicy._build at 0x7fa33ffe5090>", "forward": "<function ActorCriticPolicy.forward at 0x7fa33ffe5120>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa33ffe51b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa33ffe5240>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa33ffe52d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa33ffe5360>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa33ffe53f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa33ffe5480>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fa340b6cdc0>"}, "verbose": true, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [5], "low": "[0. 0. 0. 0. 0.]", "high": "[5.2000000e+05 3.1415927e+00 1.0000000e+02 1.0000000e+02 1.0000000e+02]", "bounded_below": "[ True  True  True  True  True]", "bounded_above": "[ 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": 204800, "_total_timesteps": 200000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1681097712738902504, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVxQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJZQAAAAAAAAACbXp0IBgcy/AADIQgAAyEIAAMhC8xRCQ4tMhT8AAMhCAADIQgAAyEKHZztCUIUAwAAAyEIAAMhCAADIQsDyOUMCtUg/AADIQgAAyEIAAMhClIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwWGlIwBQ5R0lFKULg=="}, "_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": -0.02400000000000002, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 250, "n_steps": 2048, "gamma": 0.95, "gae_lambda": 0.8, "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:": "gAWV4QIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMVy9ob21lL25vaXNlYnJpZGdlLy5sb2NhbC9saWIvcHl0aG9uMy4xMC9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgQBlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMVy9ob21lL25vaXNlYnJpZGdlLy5sb2NhbC9saWIvcHl0aG9uMy4xMC9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "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": 63.98999999999967, "std_reward": 0.0, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-09T20:40:04.968538"}