What’s Next if Reward is Enough? Insights for AGI from Animal Reinforcement Learning

Abstract

Abstract There has been considerable recent interest in the “The Reward is Enough” hypothesis, which is the idea that agents can develop general intelligence even with simple reward functions, provided the environment they operate in is sufficiently complex. While this is an interesting framework to approach the AGI problem, it also brings forth new questions - what kind of RL algorithm should the agent use? What should the reward function look like? How can it quickly generalize its learning to new tasks? This paper looks to animal reinforcement learning - both individual and social - to address these questions and more. It evaluates existing computational models and neural substrates of Pavlovian conditioning, reward-based action selection, intrinsic motivation, attention-based task representations, social learning and meta-learning in animals and discusses how insights from these findings can influence the development of animal-level AGI within an RL framework.