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arXiv Machine Learning By Erik M. Lintunen, Marlos C. Machado

Mastering Atari 2600 Games with Discovered Options

Read the original on arXiv Machine Learning →

Wayfarer is a domain‑agnostic, online deep RL agent that discovers options via Laplacian representation learning from high‑dimensional observations and uses them for control. The discovered options improve exploration, accelerate credit assignment, and generalise to unseen settings, leading to faster learning of complex policies. Wayfarer achieves state‑of‑the‑art performance among single‑stream agents on the most challenging Atari 2600 games, especially those requiring long‑horizon exploration such as Montezuma's Revenge and Private Eye.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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