Reward Is Enough (Machine Learning Research Paper Explained)
900#reinforcementlearning #deepmind #agi
What's the most promising path to creating Artificial General Intelligence (AGI)? This paper makes the bold claim that a learning agent maximizing its reward in a sufficiently complex environment will necessarily develop intelligence as a by-product, and that Reward Maximization is the best way to move the creation of AGI forward. The paper is a mix of philosophy, engineering, and futurism, and raises many points of discussion.
OUTLINE:
0:00 - Intro & Outline
4:10 - Reward Maximization
10:10 - The Reward-is-Enough Hypothesis
13:15 - Abilities associated with intelligence
16:40 - My Criticism
26:15 - Reward Maximization through Reinforcement Learning
31:30 - Discussion, Conclusion & My Comments
Paper: https://www.sciencedirect.com/science/article/pii/S0004370221000862
Abstract:
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, Richard S. Sutton
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