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[D] why the same reinforcement learning algorithm worked for MountainCar, but does not work for LunarLander (and others)

Hi Reddit community, I’m currently self-learning/exploring reinforcement learning. I have downloaded a few codes to try out and to get a feel of the code. There is a piece of code [code A] about using A3C for CartPole-v0, and it manages to learn very well. And another piece of code [code B] that uses DQN for LunarLander-v2, it managed to train a smart agent too.

Then I change the environment in code A (uses A3C) to LunarLander-v2 and MountainCar-v0, there weren’t any errors, but the agent fails to learn. Likewise, I change the environment in code B (uses DQN) to CartPole-v0 and MountainCar-v0, it didn’t learn as well.

Why is it so? Is it because different environments have different rewards system? Or the hyperparameters that worked for CartPole-v0 does not work for LunarLander-v2?

submitted by /u/ErmJustSaying
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.