Imagine dropping an AI agent into a completely new environment. Like a newborn, it would flail around, making random, unproductive moves. This is because it lacks an understanding of the environment’s underlying “laws”—the basic principles that govern how things work. New research explores how to equip AI agents with this crucial knowledge, transforming them from clueless explorers into purposeful actors. Researchers are teaching AI to learn the “laws” of their environments by studying recordings of successful gameplay. This learned experience is then used in two ways. First, for language-based AI, the rules are given as context, like handing a player a strategy guide. Second, for reinforcement learning (RL) agents, these rules are transformed into self-assigned rewards. This allows them to evaluate actions based on their understanding of the game, rather than waiting for external feedback. The researchers tested this in Crafter, a Minecraft-like game, and found that both language-based and RL agents performed significantly better when guided by these learned laws. Agents learned to collect resources, craft tools, and even defend themselves more effectively, showing a deeper understanding of the game’s dynamics. While promising, the current method uses a simplified reward system. Future research could integrate more sophisticated reward mechanisms, allowing agents to learn and adapt to even more complex challenges. This shift from blind exploration to purposeful action driven by internally understood rules represents a significant step towards more capable and adaptable AI agents.