Upload DQN LunarLander-v2 trained agent

DQN-LundarLander-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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DQN-LundarLander-v2/data

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         "__module__": "stable_baselines3.dqn.policies",
         "__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 0x7f9b391d34d0>",
-        "_build": "<function DQNPolicy._build at 0x7f9b391d3560>",
-        "make_q_net": "<function DQNPolicy.make_q_net at 0x7f9b391d35f0>",
-        "forward": "<function DQNPolicy.forward at 0x7f9b391d3680>",
-        "_predict": "<function DQNPolicy._predict at 0x7f9b391d3710>",
-        "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7f9b391d37a0>",
-        "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7f9b391d3830>",
+        "__init__": "<function DQNPolicy.__init__ at 0x7f2d36bb44d0>",
+        "_build": "<function DQNPolicy._build at 0x7f2d36bb4560>",
+        "make_q_net": "<function DQNPolicy.make_q_net at 0x7f2d36bb45f0>",
+        "forward": "<function DQNPolicy.forward at 0x7f2d36bb4680>",
+        "_predict": "<function DQNPolicy._predict at 0x7f2d36bb4710>",
+        "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7f2d36bb47a0>",
+        "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7f2d36bb4830>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f9b391d6060>"
+        "_abc_impl": "<_abc_data object at 0x7f2d36c1e840>"
     },
     "verbose": 1,
     "policy_kwargs": {
@@ -36,7 +36,7 @@
     },
     "action_space": {
         ":type:": "<class 'gym.spaces.discrete.Discrete'>",
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     "tensorboard_log": null,
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     "batch_size": 128,
     "learning_starts": 0,
     "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:\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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-        "sample": "<function ReplayBuffer.sample at 0x7f9b391a2d40>",
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+        "add": "<function ReplayBuffer.add at 0x7f2d36c05cb0>",
+        "sample": "<function ReplayBuffer.sample at 0x7f2d36c05d40>",
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         "__abstractmethods__": "frozenset()",
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+        "_abc_impl": "<_abc_data object at 0x7f2d36c686f0>"
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DQN-LundarLander-v2/policy.optimizer.pth

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 size 557935

DQN-LundarLander-v2/policy.pth

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README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 154.35 +/- 123.25
+      value: -116.80 +/- 16.36
       name: mean_reward
     task:
       type: reinforcement-learning

config.json

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results.json

@@ -1 +1 @@
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