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.