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

arXiv: https://arxiv.org/abs/2202.03192 · HTML: https://mlsanity.com/paper/2202.03192/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 60 references that are in the map.
- First posted on arXiv 3 Feb 2022.

## Builds on (most cited first, 2 of 2)

- [On the Expressivity of Markov Reward](https://mlsanity.com/paper/2111.00876/) (2021)
- [Robust Market Making via Adversarial Reinforcement Learning](https://mlsanity.com/paper/2003.01820/) (2020)

## Abstract (arXiv)

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.