all AI news
Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation
The Berkeley Artificial Intelligence Research Blog bair.berkeley.edu
Reinforcement learning provides a conceptual framework for autonomous agents to learn from experience, analogously to how one might train a pet with treats. But practical applications of reinforcement learning are often far from natural: instead of using RL to learn through trial and error by actually attempting the desired task, typical RL applications use a separate (usually simulated) training phase. For example, AlphaGo did not learn to play Go by competing against thousands of humans, but rather by playing against …
!-->agents alphago applications applications of reinforcement learning autonomous autonomous agents error example experience framework humans kind learn mobile natural playing practical reinforcement reinforcement learning simulation through training world