TWIRL: Teaching With Interactive Reinforcement Learning
Persons participating in the project
Nowadays, robotic research reaches progress in different fields using diverse learning algorithms. Varied tasks, such as navigation, grasping, vision, speech recognition, pattern recognition among others, can be tackled and represented with different machine learning paradigms, like supervised learning, unsupervised learning or reinforcement learning. Often these tasks are performed in domestic scenarios with active human participation, in order to complete them collaboratively.
Reinforcement Learning (RL) uses sequential decisions, where an agent interacts with its environment. The environment is defined as all that is out of the agent's control and not as out of the agent's boundary, in a physic sense. The agent, in each state, selects an action to be performed, and then, it receives either a reward or a punishment. The agent, over time, aims at getting the highest reward, or as high as possible. Therefore the problem is reduced to finding an appropriate policy, which allows
Reinforcement Learning has been a very useful