Synthesizing world models for bilevel planning

Understand

Modern reinforcement learning (RL) systems have demonstrated remarkable capabilities in complex environments, such as video games.

• However, they still fall short of achieving human-like sample efficiency and adaptability when learning new domains.
• Theory-based reinforcement learning (TBRL) is an algorithmic framework specifically designed to address this gap.
• Modeled on cognitive theories, TBRL leverages structured, causal world models - "theories" - as forward simulators for use in planning, generalization and exploration.