DQN lunar lander V2 trained for 500k, n_steps=2048, batch_size=128

.gitattributes

@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,28 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: DQN
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 167.08 +/- 79.19
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+---
+
+  # **DQN** Agent playing **LunarLander-v2**
+  This is a trained model of a **DQN** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+  
+  ## Usage (with Stable-baselines3)
+  TODO: Add your code
+  

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVMAAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLmRxbi5wb2xpY2llc5SMCURRTlBvbGljeZSTlC4=", "__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 0x7f6eeafaab90>", "_build": "<function DQNPolicy._build at 0x7f6eeafaac20>", "make_q_net": "<function DQNPolicy.make_q_net at 0x7f6eeafaacb0>", "forward": "<function DQNPolicy.forward at 0x7f6eeafaad40>", "_predict": "<function DQNPolicy._predict at 0x7f6eeafaadd0>", "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7f6eeafaae60>", "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7f6eeafaaef0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f6eeafeae40>"}, "verbose": 0, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": 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ishan_dqn_lunar_lander.zip

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ishan_dqn_lunar_lander/_stable_baselines3_version

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ishan_dqn_lunar_lander/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 0x7f6eeafaab90>",
+        "_build": "<function DQNPolicy._build at 0x7f6eeafaac20>",
+        "make_q_net": "<function DQNPolicy.make_q_net at 0x7f6eeafaacb0>",
+        "forward": "<function DQNPolicy.forward at 0x7f6eeafaad40>",
+        "_predict": "<function DQNPolicy._predict at 0x7f6eeafaadd0>",
+        "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7f6eeafaae60>",
+        "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7f6eeafaaef0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f6eeafeae40>"
+    },
+    "verbose": 0,
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+Python: 3.7.13
+Stable-Baselines3: 1.5.0
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