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DQN-FlappyBird-v0.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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DQN-FlappyBird-v0/data

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         "__annotations__": "{'q_net': <class 'stable_baselines3.dqn.policies.QNetwork'>, 'q_net_target': <class 'stable_baselines3.dqn.policies.QNetwork'>}",
         "__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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         "__abstractmethods__": "frozenset()",
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DQN-FlappyBird-v0/system_info.txt

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-- OS: Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-glibc2.35 # 1 SMP Fri Jan 27 02:56:13 UTC 2023
-- Python: 3.10.6
+- OS: Linux-5.15.146.1-microsoft-standard-WSL2-x86_64-with-glibc2.35 # 1 SMP Thu Jan 11 04:09:03 UTC 2024
+- Python: 3.9.18
 - Stable-Baselines3: 2.1.0
-- PyTorch: 2.0.1+cu117
+- PyTorch: 2.1.0+cpu
 - GPU Enabled: False
-- Numpy: 1.25.1
-- Cloudpickle: 2.2.1
-- Gymnasium: 0.28.1
+- Numpy: 1.26.1
+- Cloudpickle: 3.0.0
+- Gymnasium: 0.29.1
 - OpenAI Gym: 0.26.2

README.md

@@ -16,7 +16,7 @@ model-index:
       type: FlappyBird-v0
     metrics:
     - type: mean_reward
-      value: 9.53 +/- 1.04
+      value: 9.00 +/- 0.00
       name: mean_reward
       verified: false
 ---

config.json

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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 0x7fdebe34f0a0>", "_build": "<function DQNPolicy._build at 0x7fdebe34f130>", "make_q_net": "<function DQNPolicy.make_q_net at 0x7fdebe34f1c0>", "forward": "<function DQNPolicy.forward at 0x7fdebe34f250>", "_predict": "<function DQNPolicy._predict at 0x7fdebe34f2e0>", "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7fdebe34f370>", "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7fdebe34f400>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fdebe35aec0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 200000, "_total_timesteps": 200000.0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1699176789835401289, 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results.json

@@ -1 +1 @@
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