  Reinforcement learning is a powerful learning paradigm in which agents can
learn to maximize sparse and delayed reward signals. Although RL has had many
impressive successes in complex domains, learning can take hours, days, or even
years of training data. A major challenge of contemporary RL research is to
discover how to learn with less data. Previous work has shown that domain
information can be successfully used to shape the reward; by adding additional
reward information, the agent can learn with much less data. Furthermore, if
the reward is constructed from a potential function, the optimal policy is
guaranteed to be unaltered. While such potential-based reward shaping (PBRS)
holds promise, it is limited by the need for a well-defined potential function.
Ideally, we would like to be able to take arbitrary advice from a human or
other agent and improve performance without affecting the optimal policy. The
recently introduced dynamic potential based advice (DPBA) method tackles this
challenge by admitting arbitrary advice from a human or other agent and
improves performance without affecting the optimal policy. The main
contribution of this paper is to expose, theoretically and empirically, a flaw
in DPBA. Alternatively, to achieve the ideal goals, we present a simple method
called policy invariant explicit shaping (PIES) and show theoretically and
empirically that PIES succeeds where DPBA fails.

Machine Learning;Artificial Intelligence