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README.md

@@ -16,7 +16,7 @@ model-index:
       type: CartPole-v1
     metrics:
     - type: mean_reward
-      value: 152.20 +/- 2.86
+      value: 500.00 +/- 0.00
       name: mean_reward
       verified: false
 ---

args.yml

@@ -54,13 +54,13 @@
   - - save_replay_buffer
     - false
   - - seed
-    - 4205676148
+    - 2904896230
   - - storage
     - null
   - - study_name
     - null
   - - tensorboard_log
-    - runs/CartPole-v1__dqn__4205676148__1698256789
+    - runs/CartPole-v1__dqn__2904896230__1698259642
   - - track
     - true
   - - trained_agent

dqn-CartPole-v1.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:de6a6e62ebce57ab258e1e17e873086d91349745f1365c364002c193661356cf
-size 1107479
+oid sha256:6c52e0bab37152188fea3b271cee0fd2faf7752eb8521a5ed578f989737a9bf4
+size 1107467

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 0x7f7e7d8d8310>",
-        "_build": "<function DQNPolicy._build at 0x7f7e7d8d83a0>",
-        "make_q_net": "<function DQNPolicy.make_q_net at 0x7f7e7d8d8430>",
-        "forward": "<function DQNPolicy.forward at 0x7f7e7d8d84c0>",
-        "_predict": "<function DQNPolicy._predict at 0x7f7e7d8d8550>",
-        "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7f7e7d8d85e0>",
-        "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7f7e7d8d8670>",
+        "__init__": "<function DQNPolicy.__init__ at 0x7f2fa26a0310>",
+        "_build": "<function DQNPolicy._build at 0x7f2fa26a03a0>",
+        "make_q_net": "<function DQNPolicy.make_q_net at 0x7f2fa26a0430>",
+        "forward": "<function DQNPolicy.forward at 0x7f2fa26a04c0>",
+        "_predict": "<function DQNPolicy._predict at 0x7f2fa26a0550>",
+        "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7f2fa26a05e0>",
+        "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7f2fa26a0670>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7f7e7d8cadc0>"
+        "_abc_impl": "<_abc._abc_data object at 0x7f2fa268f500>"
     },
     "verbose": 1,
     "policy_kwargs": {
@@ -27,12 +27,12 @@
     "_num_timesteps_at_start": 0,
     "seed": 0,
     "action_noise": null,
-    "start_time": 1698256792389686494,
+    "start_time": 1698259645745364335,
     "learning_rate": {
         ":type:": "<class 'function'>",
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     },
-    "tensorboard_log": "runs/CartPole-v1__dqn__4205676148__1698256789/CartPole-v1",
+    "tensorboard_log": "runs/CartPole-v1__dqn__2904896230__1698259642/CartPole-v1",
     "_last_obs": null,
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -40,16 +40,16 @@
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-    "_episode_num": 379,
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     "use_sde": false,
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         "__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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+        "_abc_impl": "<_abc._abc_data object at 0x7f2fa27fcfc0>"
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     "train_freq": {

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