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Despite remarkable successes in various domains such as robotics and games, Reinforcement Learning (RL) still struggles with exploration inefficiency. For example, in hard Atari games, state-of-the-art agents often require billions of trial actions, equivalent to years of practice, while a moderately skilled human player can achieve the same score in just a few hours of play. This contrast emerges from the difference in exploration strategies between humans, leveraging memory, intuition and experience, and current RL agents, primarily relying on random trials and errors. This tutorial reviews recent advances in enhancing RL exploration efficiency through intrinsic motivation or curiosity, allowing agents to navigate environments without external rewards. Unlike previous surveys, we analyze intrinsic motivation through a memory-centric perspective, drawing parallels between human and agent curiosity, and providing a memory-driven taxonomy of intrinsic motivation approaches. The talk consists of three main parts. Part A provides a brief introduction to RL basics, delves into the historical context of the explore-exploit dilemma, and raises the challenge of exploration inefficiency. In Part B, we present a taxonomy of self-motivated agents leveraging deliberate, RAM-like, and replay memory models to compute surprise, novelty, and goal, respectively. Part C explores advanced topics, presenting recent methods using language models and causality for exploration. Whenever possible, case studies and hands-on coding demonstrations. will be presented.

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He said to them, “Go into all the world and preach the gospel to all creation. Whoever believes and is baptized will be saved, but whoever does not believe will be condemned. And these signs will accompany those who believe: In my name they will drive out demons; they will speak in new tongues; they will pick up snakes with their hands; and when they drink deadly poison, it will not hurt them at all; they will place their hands on sick people, and they will get well.” After the Lord Jesus had spoken to them, he was taken up into heaven and he sat at the right hand of God. Then the disciples went out and preached everywhere, and the Lord worked with them and confirmed his word by the signs that accompanied it. Mark 16:15-20

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User research literature widely assumes that researchers should build empathy for their users, and that this will enable companies to design better experiences. Businesses point to UX research as the empathy in their company ethos. But when we talk about empathy in a business context, what do we really mean? Is it reasonable to expect researchers to empathize with every person they interview? Is it even productive? In this presentation, I will discuss why empathy is not what drives business and makes researchers good advocates for our users. We will first look at the literal and theoretical definitions of empathy in user experience research, then draw from that definition what businesses are ultimately in search of—rightly or wrongly—when they bring UX research into the process. We will talk about how it may not only be impossible to achieve true empathy, but also that “being empathetic” may not make researchers better advocates for the user. I will argue that we should reframe our goal as compassion, an action-driven, thoughtful, and achievable north star that leverages core researcher talents and better communicates user needs.

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