AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers

Reinforcement Learning (RL) algorithms have recently demonstrated impressive results in challenging problem domains such as robotic manipulation, Go, and Atari games. But, RL algorithms typically require a large number of interactions with the environment to train policies that solve new tasks, since they begin with no knowledge whatsoever about the task and rely on random exploration of their possible actions in order to learn. This is particularly problematic for physical domains such as robotics, where gathering experience from interactions is slow and expensive. At the same time, people often have some intution about the right kinds of things to do during RL tasks, such as approaching an object when attempting to grasp it – might it be possible for us to somehow communicate these intuitions to the RL agent to speed up its training?