Upload DQN Mountain Car trained agent

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.gitattributes +1 -0
MountainCar-v0_dqn_1_million.zip +3 -0
MountainCar-v0_dqn_1_million/_stable_baselines3_version +1 -0
MountainCar-v0_dqn_1_million/data +115 -0
MountainCar-v0_dqn_1_million/policy.optimizer.pth +3 -0
MountainCar-v0_dqn_1_million/policy.pth +3 -0
MountainCar-v0_dqn_1_million/pytorch_variables.pth +3 -0
MountainCar-v0_dqn_1_million/system_info.txt +7 -0
README.md +36 -0
config.json +1 -0
replay.mp4 +3 -0
results.json +1 -0

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MountainCar-v0_dqn_1_million/data ADDED Viewed

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+{
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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    ",
+        "__init__": "<function DQNPolicy.__init__ at 0x7f833ccacc20>",
+        "_build": "<function DQNPolicy._build at 0x7f833ccaccb0>",
+        "make_q_net": "<function DQNPolicy.make_q_net at 0x7f833ccacd40>",
+        "forward": "<function DQNPolicy.forward at 0x7f833ccacdd0>",
+        "_predict": "<function DQNPolicy._predict at 0x7f833ccace60>",
+        "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7f833ccacef0>",
+        "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7f833ccacf80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f833cd09fc0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
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+        "dtype": "float32",
+        "_shape": [
+            2
+        ],
+        "low": "[-1.2  -0.07]",
+        "high": "[0.6  0.07]",
+        "bounded_below": "[ True  True]",
+        "bounded_above": "[ True  True]",
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+}

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MountainCar-v0_dqn_1_million/system_info.txt ADDED Viewed

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+OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
+Python: 3.7.13
+Stable-Baselines3: 1.5.0
+PyTorch: 1.11.0+cu113
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

README.md ADDED Viewed

	@@ -0,0 +1,36 @@

+---
+library_name: stable-baselines3
+tags:
+- MountainCar-v0
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: DQN
+  results:
+  - metrics:
+    - type: mean_reward
+      value: -200.00 +/- 0.00
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: MountainCar-v0
+      type: MountainCar-v0
+---
+# **DQN** Agent playing **MountainCar-v0**
+This is a trained model of a **DQN** agent playing **MountainCar-v0**
+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 ADDED Viewed

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