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DQN-CartPole-v1.zip

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

@@ -5,15 +5,15 @@
         "__module__": "stable_baselines3.dqn.policies",
         "__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    ",
-        "__init__": "<function DQNPolicy.__init__ at 0x2a0c7a700>",
-        "_build": "<function DQNPolicy._build at 0x2a0c7a790>",
-        "make_q_net": "<function DQNPolicy.make_q_net at 0x2a0c7a820>",
-        "forward": "<function DQNPolicy.forward at 0x2a0c7a8b0>",
-        "_predict": "<function DQNPolicy._predict at 0x2a0c7a940>",
-        "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x2a0c7a9d0>",
-        "set_training_mode": "<function DQNPolicy.set_training_mode at 0x2a0c7aa60>",
+        "__init__": "<function DQNPolicy.__init__ at 0x3237bb5e0>",
+        "_build": "<function DQNPolicy._build at 0x3237bb670>",
+        "make_q_net": "<function DQNPolicy.make_q_net at 0x3237bb700>",
+        "forward": "<function DQNPolicy.forward at 0x3237bb790>",
+        "_predict": "<function DQNPolicy._predict at 0x3237bb820>",
+        "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x3237bb8b0>",
+        "set_training_mode": "<function DQNPolicy.set_training_mode at 0x3237bb940>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x2a0c7df00>"
+        "_abc_impl": "<_abc._abc_data object at 0x3237bfcc0>"
     },
     "verbose": 1,
     "policy_kwargs": {
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     "seed": null,
     "action_noise": null,
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     "learning_rate": 0.0001,
     "tensorboard_log": null,
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DQN-CartPole-v1/policy.optimizer.pth

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DQN-CartPole-v1/policy.pth

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DQN-CartPole-v1/system_info.txt

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-- OS: macOS-14.2.1-arm64-arm-64bit Darwin Kernel Version 23.2.0: Wed Nov 15 21:53:34 PST 2023; root:xnu-10002.61.3~2/RELEASE_ARM64_T8103
+- OS: macOS-14.4.1-arm64-arm-64bit Darwin Kernel Version 23.4.0: Fri Mar 15 00:12:41 PDT 2024; root:xnu-10063.101.17~1/RELEASE_ARM64_T8103
 - Python: 3.9.19
 - Stable-Baselines3: 2.1.0
 - PyTorch: 2.2.1

README.md

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       type: CartPole-v1
     metrics:
     - type: mean_reward
-      value: 213.40 +/- 20.11
+      value: 203.20 +/- 81.06
       name: mean_reward
       verified: false
 ---

config.json

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https://github.com/DLR-RM/stable-baselines3/issues/284\n    ", "__init__": "<function ReplayBuffer.__init__ at 0x323793e50>", "add": "<function ReplayBuffer.add at 0x323793ee0>", "sample": "<function ReplayBuffer.sample at 0x323793f70>", "_get_samples": "<function ReplayBuffer._get_samples at 0x3237a0040>", "_maybe_cast_dtype": "<staticmethod object at 0x32379f250>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x3237a1080>"}, "replay_buffer_kwargs": {}, "train_freq": {":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>", ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLBGgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"}, "use_sde_at_warmup": false, "exploration_initial_eps": 1.0, "exploration_final_eps": 0.05, "exploration_fraction": 0.1, "target_update_interval": 10000, "_n_calls": 100000, "max_grad_norm": 10, "exploration_rate": 0.05, "lr_schedule": {":type:": "<class 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results.json

@@ -1 +1 @@
-{"mean_reward": 213.4, "std_reward": 20.110693672770218, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-04-09T09:51:32.824633"}
+{"mean_reward": 203.2, "std_reward": 81.06022452473222, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-04-14T15:16:58.051434"}