Policy transformation for learning from demonstration

Many different robot learning from demonstration methods have been applied and tested in various environments recently. Representation of learned plans, tasks and policies often depends on the technique due to method-specific parameters. An agent that is able to switch between representations can apply its knowledge to different algorithms. This flexibility can be useful for a human teacher when training the agent. In this work we present a process to convert learned policies with two specific methods, Confidence-Based Autonomy (CBA) and Interactive Reinforcement Learning (Int-RL), to each other. Our finding suggests that it is possible for an agent to learn a policy with either CBA or Int-RL method and execute the task with the other with the benefit of previously learned knowledge.