Facebook’s RIDE encourages AI agents to explore their environments
A preprint paper coauthored by scientists at Facebook AI Research describes Rewarding Impact-Driven Exploration (RIDE), an intrinsic reward method that encourages AI-driven agents to take actions in an environment. The researchers say that it outperforms state-of-the-art methods on hard exploration tasks in procedurally generated worlds, a sign it might be a candidate for devices like robot vacuums that must often navigate new environments.
As the researchers explain, reinforcement learning, where the goal is to spur an agent to complete tasks via systems of rewards, learn to act in new environments through trial and error. But many environments of interest particularly those closer to real-world problems don’t provide a steady stream of rewards for agents to learn from, requiring many episodes before agents come across rewards.
