DeepMind scientists: Reinforcement learning is enough for general AI

reinforcement-learningagireward-maximizationdeepmind

Abstraction: DeepMind paper arguing reward maximization alone yields AGI

Key points:

DeepMind paper "Reward is Enough" (Artificial Intelligence journal) argues that maximizing reward through trial-and-error is sufficient to produce all abilities associated with intelligence
Draws analogy to natural selection: simple fitness objectives produced complex behaviors like perception, language, and social intelligence without top-down design
RL proposed as the primary mechanism: an agent that continually improves to maximize cumulative reward will develop perception, language, and social skills implicitly
Key unsolved challenges acknowledged: sample inefficiency (may need centuries of gameplay), poor cross-domain generalization, and undefined reward/action specifications
Critic Patricia Churchland called it a "milestone" but noted bonding/attachment as a missing factor; critic Herbert Roitblat argued reward maximization presupposes the very representations it claims to produce

Connections: Deepmind · Reinforcement Learning · Artificial General Intelligence · Reward Maximization