Eureka’s Zero-Shot Reward Generation
Understand how Eureka uses environment code as input to generate meaningful reward functions in zero-shot, demonstrating a powerful example of context-grounded agent behavior.
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In reinforcement learning (RL), agents learn to perform tasks by interacting with an environment and receiving feedback in the form of rewards. But before an agent can effectively learn, a precise reward function (a piece of code defining what success looks like) must be written. In many real-world scenarios, especially when dealing with novel environments or tasks, no pre-existing reward function exists. This often means there are no sample outputs or human-written instructions to guide the design process; only the environment’s raw source code is available to describe how the world operates.
The fundamental challenge then becomes: how can an AI agent understand a new environment from scratch and autonomously generate an effective reward function? Eureka tackles this initial challenge in its design loop: generating a reward function in a zero-shot setting, using only the environment’s definition as context. This capability directly relates to the perception and input interface aspects of an agent, where it must transform raw environmental data into meaningful signals for its reasoning core.
How Eureka uses code as context
As we saw in the previous lesson’s architectural overview of Eureka, the system begins by consuming raw environment code and a task description. In this lesson, we’ll focus on that crucial first step: how the agent leverages this context to autonomously generate meaningful reward functions in a zero-shot manner.
To overcome the problem of generating a reward function with no examples or prior explicit knowledge, ...