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Achieving Human-Level Atari Gameplay with Object-Centric Priors and Active Inference
Author: Chris Buckley
January 20, 2025
Summary
VERSES AI Research Blog – “It has been several years since deep reinforcement learning (DRL) algorithms first demonstrated superhuman performance in complex environments. One of the most seminal achievements was mastering Atari games using DRL architectures that learned directly from pixel inputs and reward signals (Mnih et al., 2015). However, these models required millions of data frames—equivalent to months of gameplay—and significant computational resources. This high sample and compute cost limited their applicability to real-world challenges and raised questions about their efficiency compared to human learning.”