Upload best DQN MountainCar-v0 agent (tuned with Optuna).

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

MountainCar-v0-DQN-optuna.zip

@@ -0,0 +1,3 @@
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MountainCar-v0-DQN-optuna/_stable_baselines3_version

@@ -0,0 +1 @@
+1.5.0

MountainCar-v0-DQN-optuna/data

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+{
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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    ",
+        "__init__": "<function DQNPolicy.__init__ at 0x7fb43c9a0290>",
+        "_build": "<function DQNPolicy._build at 0x7fb43c9a0320>",
+        "make_q_net": "<function DQNPolicy.make_q_net at 0x7fb43c9a03b0>",
+        "forward": "<function DQNPolicy.forward at 0x7fb43c9a0440>",
+        "_predict": "<function DQNPolicy._predict at 0x7fb43c9a04d0>",
+        "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7fb43c9a0560>",
+        "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7fb43c9a05f0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fb43c987960>"
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+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
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+        "_shape": [
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+        "low": "[-1.2  -0.07]",
+        "high": "[0.6  0.07]",
+        "bounded_below": "[ True  True]",
+        "bounded_above": "[ True  True]",
+        "_np_random": null
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+    "learning_rate": 0.0001,
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+    }
+}

MountainCar-v0-DQN-optuna/policy.optimizer.pth

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MountainCar-v0-DQN-optuna/policy.pth

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MountainCar-v0-DQN-optuna/pytorch_variables.pth

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MountainCar-v0-DQN-optuna/system_info.txt

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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

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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
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

config.json

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replay.mp4

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1365291887066273cc4ae9f8485c8e01643e519bc7a8984c43f5bc1dd0298c5e
+size 156553

results.json

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+{"mean_reward": -200.0, "std_reward": 0.0, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-06-25T09:39:42.908757"}