Upload DQNCartPole-v1 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: CartPole-v1
     metrics:
     - type: mean_reward
-      value: 290.70 +/- 12.64
+      value: 9.40 +/- 0.80
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
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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 0x786303c7f010>", "_build": "<function DQNPolicy._build at 0x786303c7f0a0>", "make_q_net": "<function DQNPolicy.make_q_net at 0x786303c7f130>", "forward": "<function DQNPolicy.forward at 0x786303c7f1c0>", "_predict": "<function DQNPolicy._predict at 0x786303c7f250>", "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x786303c7f2e0>", "set_training_mode": "<function DQNPolicy.set_training_mode at 0x786303c7f370>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x786303c89e80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1718245312595397687, 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https://github.com/DLR-RM/stable-baselines3/issues/284\n    ", "__init__": "<function ReplayBuffer.__init__ at 0x786303c6f490>", "add": "<function ReplayBuffer.add at 0x786303c6f520>", "sample": "<function ReplayBuffer.sample at 0x786303c6f5b0>", "_get_samples": "<function ReplayBuffer._get_samples at 0x786303c6f640>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x786303c70fc0>"}, "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": 625, "_n_calls": 62500, "max_grad_norm": 10, "exploration_rate": 0.05, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": 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https://github.com/DLR-RM/stable-baselines3/issues/284\n    ", "__init__": "<function ReplayBuffer.__init__ at 0x7bdabe698040>", "add": "<function ReplayBuffer.add at 0x7bdabe6980d0>", "sample": "<function ReplayBuffer.sample at 0x7bdabe698160>", "_get_samples": "<function ReplayBuffer._get_samples at 0x7bdabe6981f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7bdabe694cc0>"}, "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": 0.01, "exploration_fraction": 0.1, "target_update_interval": 62, "_n_calls": 62500, "max_grad_norm": 10, "exploration_rate": 0.01, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": 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dqn-CartPole-v1.zip

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dqn-CartPole-v1/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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@@ -76,11 +76,11 @@
         "_np_random": "Generator(PCG64)"
     },
     "n_envs": 16,
-    "buffer_size": 1000000,
+    "buffer_size": 50000,
     "batch_size": 32,
-    "learning_starts": 50000,
+    "learning_starts": 1000,
     "tau": 1.0,
-    "gamma": 0.6,
+    "gamma": 0.99,
     "gradient_steps": 1,
     "optimize_memory_usage": false,
     "replay_buffer_class": {
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         "__module__": "stable_baselines3.common.buffers",
         "__doc__": "\n    Replay buffer used in off-policy algorithms like SAC/TD3.\n\n    :param buffer_size: Max number of element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param device: PyTorch device\n    :param n_envs: Number of parallel environments\n    :param optimize_memory_usage: Enable a memory efficient variant\n        of the replay buffer which reduces by almost a factor two the memory used,\n        at a cost of more complexity.\n        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n        and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n        Cannot be used in combination with handle_timeout_termination.\n    :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n        separately and treat the task as infinite horizon task.\n        https://github.com/DLR-RM/stable-baselines3/issues/284\n    ",
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-        "add": "<function ReplayBuffer.add at 0x786303c6f520>",
-        "sample": "<function ReplayBuffer.sample at 0x786303c6f5b0>",
-        "_get_samples": "<function ReplayBuffer._get_samples at 0x786303c6f640>",
+        "__init__": "<function ReplayBuffer.__init__ at 0x7bdabe698040>",
+        "add": "<function ReplayBuffer.add at 0x7bdabe6980d0>",
+        "sample": "<function ReplayBuffer.sample at 0x7bdabe698160>",
+        "_get_samples": "<function ReplayBuffer._get_samples at 0x7bdabe6981f0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x786303c70fc0>"
+        "_abc_impl": "<_abc._abc_data object at 0x7bdabe694cc0>"
     },
     "replay_buffer_kwargs": {},
     "train_freq": {
@@ -102,12 +102,12 @@
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     "use_sde_at_warmup": false,
     "exploration_initial_eps": 1.0,
-    "exploration_final_eps": 0.05,
+    "exploration_final_eps": 0.01,
     "exploration_fraction": 0.1,
-    "target_update_interval": 625,
+    "target_update_interval": 62,
     "_n_calls": 62500,
     "max_grad_norm": 10,
-    "exploration_rate": 0.05,
+    "exploration_rate": 0.01,
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dqn-CartPole-v1/policy.optimizer.pth

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+size 42272

dqn-CartPole-v1/policy.pth

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dqn-CartPole-v1/system_info.txt

@@ -2,8 +2,8 @@
 - Python: 3.10.12
 - Stable-Baselines3: 2.0.0a5
 - PyTorch: 2.3.0+cu121
-- GPU Enabled: False
+- GPU Enabled: True
 - Numpy: 1.25.2
 - Cloudpickle: 2.2.1
 - Gymnasium: 0.28.1
-- OpenAI Gym: 0.25.2
+- OpenAI Gym: 0.26.2

results.json

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