Upload DQN LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: -136.19 +/- 29.42
+      value: 50.78 +/- 139.96
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
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dqn-LunarLander-v2-seed42-3.zip

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         ":type:": "<class 'collections.deque'>",
@@ -76,10 +76,10 @@
     },
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     "buffer_size": 10000,
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     "learning_starts": 10000,
     "tau": 1.0,
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     "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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         "__abstractmethods__": "frozenset()",
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+        "_abc_impl": "<_abc._abc_data object at 0x0000014D3DD18240>"
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results.json

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-{"mean_reward": -136.18846876376847, "std_reward": 29.42360504175667, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-05T14:56:25.267968"}
+{"mean_reward": 50.783393684728914, "std_reward": 139.9631077019789, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-04-05T15:26:04.559812"}