Upload DQN MountainCar-v0 trained agent

.gitattributes

@@ -30,3 +30,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

DQN-Mlp.zip

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DQN-Mlp/_stable_baselines3_version

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DQN-Mlp/data

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+        "__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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+        "make_q_net": "<function DQNPolicy.make_q_net at 0x7f95c2dc3160>",
+        "forward": "<function DQNPolicy.forward at 0x7f95c2dc31f0>",
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+}

DQN-Mlp/policy.optimizer.pth

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+size 40539

DQN-Mlp/policy.pth

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DQN-Mlp/pytorch_variables.pth

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DQN-Mlp/system_info.txt

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+OS: Linux-5.15.0-50-generic-x86_64-with-glibc2.31 #56~20.04.1-Ubuntu SMP Tue Sep 27 15:51:29 UTC 2022
+Python: 3.9.12
+Stable-Baselines3: 1.5.0
+PyTorch: 1.8.1
+GPU Enabled: True
+Numpy: 1.21.5
+Gym: 0.21.0

README.md

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+---
+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
+  

config.json

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replay.mp4

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+oid sha256:1e9972a62a0260e3b473906d81de74aa3695ff3013ff2d66ac09fc131a347cd0
+size 164045

results.json

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+{"mean_reward": -200.0, "std_reward": 0.0, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-10-12T23:17:11.530724"}