DeepMind says reinforcement learning is ‘enough’ to reach general AI
Reinforcement learning is a special branch of AI algorithms that is composed of three key elements: an environment, agents, and rewards. In their paper, the researchers at DeepMind suggest reinforcement learning as the main algorithm that can replicate reward maximization as seen in nature and can eventually lead to artificial general intelligence. In the paper, the researchers provide several examples that show how reinforcement learning agents were able to learn general skills in games and robotic environments. They say, "We do not offer any theoretical guarantee on the sample efficiency of reinforcement learning agents." Reinforcement learning is notoriously renowned for requiring huge amounts of data. AI researchers still haven't figured out how to create reinforcement learning systems that can generalize their learnings across several domains. "Reinforcement learning assumes that the agent has a finite set of potential actions. A reward signal and value function have been specified. In other words, the problem of general intelligence is precisely to contribute those things that reinforcement learning requires as a pre-requisite," Roitblat said. "So, if machine learning can all be reduced to some form of optimization to maximize some evaluative measure, then it must be true that reinforcement learning is relevant, but it is not very explanatory."