Unifying learning from preferences and demonstration via a ranking game for imitation learning
For many people, opening door handles or moving a pen between their fingers is a movement that happens multiple times a day, often without much thought. For a robot, however, these movements aren’t always so easy.
In reinforcement learning, robots learn to perform tasks by exploring their environments, receiving signals along the way that indicate how good their behavior is compared to the desired outcome, or state. For the described movements, for example, we can specify a reward function that is +1 when the door is successfully opened or the