Reward is enough

In this article we hypothesise that intelligence, and its associated abilities, can be understood as subserving the maximisation of reward. Accordingly, reward is enough to drive behaviour that exhibits abilities studied in natural and artificial intelligence, including knowledge, learning, perception, social intelligence, language, generalisation and imitation. This is in contrast to the view that specialised problem formulations are needed for each ability, based on other signals or objectives. Furthermore, we suggest that agents that learn through trial and error experience to maximise reward could learn behaviour that exhibits most if not all of these abilities, and therefore that powerful reinforcement learning agents could constitute a solution to artificial general intelligence.

Authors

• David Silver
• Satinder Singh
• Doina Precup Mila – Quebec Artificial Intelligence Institute & McGill University
• Richard S. Sutton DeepMind, Edmonton, Alberta, Canada University of Alberta, Edmonton, Alberta, Canada Alberta Machine Intelligence Institute (Amii), Edmonton, Alberta, Canada Canada CIFAR AI Chair, Canada

Keywords

• Artificial intelligence
• Artificial general intelligence
• Reinforcement learning
• Reward