Initial commit

README.md

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+---
+library_name: stable-baselines3
+tags:
+- Taxi-v3
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: DQN
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: Taxi-v3
+      type: Taxi-v3
+    metrics:
+    - type: mean_reward
+      value: -200.00 +/- 0.00
+      name: mean_reward
+      verified: false
+---
+
+# **DQN** Agent playing **Taxi-v3**
+This is a trained model of a **DQN** agent playing **Taxi-v3**
+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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the learning rate, to pass to the optimizer\n    ", "__init__": "<function DQNPolicy.__init__ at 0x7f1292256e50>", "_build": "<function DQNPolicy._build at 0x7f1292256ee0>", "make_q_net": "<function DQNPolicy.make_q_net at 0x7f1292256f70>", "forward": "<function DQNPolicy.forward at 0x7f129225f040>", "_predict": "<function DQNPolicy._predict at 0x7f129225f0d0>", "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7f129225f160>", "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7f129225f1f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f129225e280>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVgwAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRN9AGMBl9zaGFwZZQpjAVkdHlwZZSMBW51bXB5lGgHk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMCl9ucF9yYW5kb22UTnViLg==", "n": 500, "_shape": [], "dtype": 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dqn-Taxi-v3.zip

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dqn-Taxi-v3/_stable_baselines3_version

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dqn-Taxi-v3/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    ",
+        "__init__": "<function DQNPolicy.__init__ at 0x7f1292256e50>",
+        "_build": "<function DQNPolicy._build at 0x7f1292256ee0>",
+        "make_q_net": "<function DQNPolicy.make_q_net at 0x7f1292256f70>",
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+        "__abstractmethods__": "frozenset()",
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+        "n": 500,
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+        "dtype": "int64",
+        "_np_random": null
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+}

dqn-Taxi-v3/policy.optimizer.pth

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+size 297711

dqn-Taxi-v3/policy.pth

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dqn-Taxi-v3/pytorch_variables.pth

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dqn-Taxi-v3/system_info.txt

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+- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: False
+- Numpy: 1.22.4
+- Gym: 0.21.0

results.json

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+{"mean_reward": -200.0, "std_reward": 0.0, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-15T08:17:33.445170"}