Upload DQN LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 110.47 +/- 82.96
+      value: 105.21 +/- 93.66
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
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dqn-LunarLander-v2-seed42-2.zip

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dqn-LunarLander-v2-seed42-2/data

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-    "_n_updates": 154688,
-    "buffer_size": 100000,
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+    "buffer_size": 10000,
     "batch_size": 32,
-    "learning_starts": 100000,
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     "tau": 1.0,
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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    ",
-        "__init__": "<function ReplayBuffer.__init__ at 0x00000208CA268430>",
-        "add": "<function ReplayBuffer.add at 0x00000208CA2684C0>",
-        "sample": "<function ReplayBuffer.sample at 0x00000208CA268550>",
-        "_get_samples": "<function ReplayBuffer._get_samples at 0x00000208CA2685E0>",
+        "__init__": "<function ReplayBuffer.__init__ at 0x000002AD34C57430>",
+        "add": "<function ReplayBuffer.add at 0x000002AD34C574C0>",
+        "sample": "<function ReplayBuffer.sample at 0x000002AD34C57550>",
+        "_get_samples": "<function ReplayBuffer._get_samples at 0x000002AD34C575E0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x00000208C8D5F100>"
+        "_abc_impl": "<_abc._abc_data object at 0x000002AD34C558C0>"
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     "train_freq": {
@@ -105,7 +105,7 @@
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     "exploration_fraction": 0.1,
     "target_update_interval": 3,
-    "_n_calls": 625000,
+    "_n_calls": 62500,
     "max_grad_norm": 10,
     "exploration_rate": 0.01,
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results.json

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-{"mean_reward": 110.47168229075373, "std_reward": 82.95772721378168, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-05T14:11:45.908486"}
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