DeepMind says reinforcement learning is ‘enough’ to reach general AI
In many reinforcement learning problems, the agent has no initial knowledge of the environment and starts by taking random actions. For instance, 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. For instance, a reinforcement learning agent might need centuries worth of gameplay to master a computer game. And AI researchers still haven’t figured out how to create reinforcement learning systems that can generalize their learnings across several domains. In other words, the problem of general intelligence is precisely to contribute those things that reinforcement learning requires as a pre-requisite,” Roitblat said.