culteejen commited on
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Upload model to Hugging Face

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BC-harcodemap-punish-stagnant.zip ADDED
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+ "__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 ",
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+ "clip_range_vf": null,
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+ "normalize_advantage": true,
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+ "target_kl": null
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+ }
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+ size 18973
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+ - 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
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+ - Python: 3.10.9
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+ - Stable-Baselines3: 1.7.0
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+ - PyTorch: 2.0.0
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+ - GPU Enabled: True
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+ - Numpy: 1.23.5
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+ - Gym: 0.21.0
README.md ADDED
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1
+ ---
2
+ library_name: stable-baselines3
3
+ tags:
4
+ - RoombaAToB-harcodemap-punish-stagnant
5
+ - deep-reinforcement-learning
6
+ - reinforcement-learning
7
+ - stable-baselines3
8
+ model-index:
9
+ - name: BC
10
+ results:
11
+ - task:
12
+ type: reinforcement-learning
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+ name: reinforcement-learning
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+ dataset:
15
+ name: RoombaAToB-harcodemap-punish-stagnant
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+ type: RoombaAToB-harcodemap-punish-stagnant
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+ metrics:
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+ - type: mean_reward
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+ value: -409.99 +/- 0.00
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+ name: mean_reward
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+ verified: false
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+ ---
23
+
24
+ # **BC** Agent playing **RoombaAToB-harcodemap-punish-stagnant**
25
+ This is a trained model of a **BC** agent playing **RoombaAToB-harcodemap-punish-stagnant**
26
+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
27
+
28
+ ## Usage (with Stable-baselines3)
29
+ TODO: Add your code
30
+
31
+
32
+ ```python
33
+ from stable_baselines3 import ...
34
+ from huggingface_sb3 import load_from_hub
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+
36
+ ...
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+ ```
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Binary file (950 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
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