Surprising Negative Results for Generative Adversarial Tree Search
Kamyar Azizzadenesheli; Brandon Yang; Weitang Liu; Zachary C Lipton; Animashree Anandkumar · 2018 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
While many recent advances in deep reinforcement learning (RL) rely on model-free methods, model-based approaches remain an alluring prospect for their potential to exploit unsupervised data to learn environment model. In this work, we provide an extensive study on the design of deep generative models for RL environments and propose a sample efficient and robust method to learn the model of Atari environments. We deploy this model and propose generative adversarial tree search (GATS) a deep RL algorithm that learns the environment model and implements Monte Carlo tree search (MCTS) on the lear
Excerpt shown for reference under fair use — read the full paper at the publisher.
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Metadata source: arXiv