Reward Is Enough

This video is a short commentary on the paper Reward Is Enough, authored by researchers at DeepMind, which argues that reward maximization provides a sufficiently general objective for intelligent behavior. The paper examines whether reinforcement learning, when combined with rich environments and scalable function approximation, can in principle give rise to the full range of cognitive capabilities associated with intelligence. The discussion focuses on the core thesis of the paper: that many abilities often treated as separate components—such as perception, planning, memory, and abstraction—can be understood as emerging from reward-driven interaction with an environment. The video also considers the assumptions underlying this claim, the distinction between capability and practicality, and the implications for artificial general intelligence research. Rather than presenting a definitive answer, the commentary aims to clarify what “reward is enough” means in a technical sense and where open questions and limitations remain. Referenced paper: * Silver et al., Reward Is Enough: https://www.sciencedirect.com/science/article/pii/S0004370221000862 Related videos: * Human-level control through deep reinforcement learning – Intro: https://www.youtube.com/watch?v=A0OVmImyFEA * Deterministic Policy Gradient Algorithms – Theory: https://www.youtube.com/watch?v=bVUII67H-M4 * Deterministic Policy Gradient Algorithms (playlist): https://www.youtube.com/playlist?list=PL3PIRsM7569PX_TzvDG6FaZz4KwRbIzsy Feel free to share your thoughts or critiques of the paper in the comments.