Reward is not enough: can we liberate AI from the reinforcement learning paradigm?

TL;DR

This paper argues that reinforcement learning, centered on reward maximization, is insufficient to explain or develop true intelligence, emphasizing the need for broader frameworks in AI development.

Contribution

It challenges the dominance of reward-based paradigms in AI, advocating for alternative approaches to understanding and creating intelligent systems.

Findings

Reward maximization does not fully account for intelligence.

Reinforcement learning is an incomplete framework for AI.

Broader models are necessary for safe and robust AI development.

Abstract

I present arguments against the hypothesis put forward by Silver, Singh, Precup, and Sutton ( https://www.sciencedirect.com/science/article/pii/S0004370221000862 ) : reward maximization is not enough to explain many activities associated with natural and artificial intelligence including knowledge, learning, perception, social intelligence, evolution, language, generalisation and imitation. I show such reductio ad lucrum has its intellectual origins in the political economy of Homo economicus and substantially overlaps with the radical version of behaviourism. I show why the reinforcement learning paradigm, despite its demonstrable usefulness in some practical application, is an incomplete framework for intelligence -- natural and artificial. Complexities of intelligent behaviour are not simply second-order complications on top of reward maximisation. This fact has profound implications for the development of practically usable, smart, safe and robust artificially intelligent agents.