Learning to fly by combining reinforcement learning with behavioural cloning
Eduardo F. Morales, Claude Sammut · 2004
Reinforcement learning deals with learning optimal or near optimal policies while inter-acting with the environment. Application domains with many continuous variables are dicult to solve with existing reinforcement learning methods due to the large search space. In this paper, we use a relational rep-resentation to dene powerful abstractions that allow us to incorporate domain knowl-edge and re-use previously learned policies in other similar problems. We also describe how to learn useful actions from human traces us-ing a behavioural cloning approach combined with an exploration phase. Since several con-icting actions may be induced for the same abstract state, reinforcement learning is used to learn an optimal policy over this reduced space. It is shown experimentally how a com-bination of behavioural cloning and reinforce-ment learning using a relational representa-tion is powerful enough to learn how to y an aircraft through dierent points in space and dierent turbulence conditions. 1.