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DQN-default.zip +3 -0
DQN-default/_stable_baselines3_version +1 -0
DQN-default/data +117 -0
DQN-default/policy.optimizer.pth +3 -0
DQN-default/policy.pth +3 -0
DQN-default/pytorch_variables.pth +3 -0
DQN-default/system_info.txt +7 -0
README.md +37 -0
config.json +1 -0
replay.mp4 +0 -0
results.json +1 -0

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DQN-default/_stable_baselines3_version ADDED Viewed

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DQN-default/data ADDED Viewed

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+        "__module__": "stable_baselines3.dqn.policies",
+        "__doc__": "\n    Policy class with Q-Value Net and target net for DQN\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 features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\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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+        "_build": "<function DQNPolicy._build at 0x7f738857f5b0>",
+        "make_q_net": "<function DQNPolicy.make_q_net at 0x7f738857f640>",
+        "forward": "<function DQNPolicy.forward at 0x7f738857f6d0>",
+        "_predict": "<function DQNPolicy._predict at 0x7f738857f760>",
+        "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7f738857f7f0>",
+        "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7f738857f880>",
+        "__abstractmethods__": "frozenset()",
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+    "batch_norm_stats_target": []
+}

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DQN-default/system_info.txt ADDED Viewed

	@@ -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 ADDED Viewed

	@@ -0,0 +1,37 @@

+---
+library_name: stable-baselines3
+tags:
+- RoombaAToB
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: DQN
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: RoombaAToB
+      type: RoombaAToB
+    metrics:
+    - type: mean_reward
+      value: -49.98 +/- 17.19
+      name: mean_reward
+      verified: false
+---
+# **DQN** Agent playing **RoombaAToB**
+This is a trained model of a **DQN** agent playing **RoombaAToB**
+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
+...
+```

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Binary file (910 kB). View file

results.json ADDED Viewed

	@@ -0,0 +1 @@


1	+ {"mean_reward": -49.9791662, "std_reward": 17.19289742746353, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-08T14:44:37.664576"}