Data Efficient Policy Search using PILCO and Directed Exploration
Reinforcement learning (RL) algorithms solve general sequential decision making problems through learning by trial and error. Many reinforcement learning algorithms are proven to find a good or optimal controller, but may take many interactions with the environment to do so. For real world tasks, this is often impractical, as letting a learner interact with the environment takes time and can be costly.