  Agent advising is one of the main approaches to improve agent learning
performance by enabling agents to share advice. Existing advising methods have
a common limitation that an adviser agent can offer advice to an advisee agent
only if the advice is created in the same state as the advisee's concerned
state. However, in complex environments, it is a very strong requirement that
two states are the same, because a state may consist of multiple dimensions and
two states being the same means that all these dimensions in the two states are
correspondingly identical. Therefore, this requirement may limit the
applicability of existing advising methods to complex environments. In this
paper, inspired by the differential privacy scheme, we propose a differential
advising method which relaxes this requirement by enabling agents to use advice
in a state even if the advice is created in a slightly different state.
Compared with existing methods, agents using the proposed method have more
opportunity to take advice from others. This paper is the first to adopt the
concept of differential privacy on advising to improve agent learning
performance instead of addressing security issues. The experimental results
demonstrate that the proposed method is more efficient in complex environments
than existing methods.
