Reward Learning for AI Agents: Selection, Reflection, and Feedback

Designing effective reward functions is one of the most challenging aspects of training intelligent agents. Without proper alignment, agents often optimize for unintended behaviors instead of the desired outcomes. This text-only course guides you through the foundational principles of reward design and alignment. You will learn how to implement selection, reflection, and human feedback loops to guide agent behavior reliably using modern Agent Development Kit (ADK) concepts and Eureka-style reward optimization. What you'll learn: - Understand the core concepts of reward learning, alignment, and the reward design problem. - Implement selection mechanisms to choose the most effective reward functions for specific tasks. - Apply reflection techniques that allow AI agents to evaluate and self-correct their own performance. - Integrate human feedback loops to align agent behavior with human preferences and values. - Explore Eureka-style reward learning systems for automated, LLM-driven reward generation. - Configure agent development kits (ADK) to build, test, and refine reinforcement learning environments. Starting with basic reward theory, the course moves step-by-step through practical written tutorials and architectural code snippets. You will study how to orchestrate feedback loops and evaluate agent alignment through detailed text-based walkthroughs. This course is designed for beginner to intermediate AI developers and software engineers interested in agent alignment. No advanced background in machine learning theory is required, though basic Python knowledge is helpful. Start reading today to master the art of building aligned and reflective AI agents.