Social Interpretable Reinforcement Learning
Leonardo Lucio Custode Giovanni Iacca
Reinforcement Learning (RL) bears the promise of being an enabling technology
for many applications. However, since most of the literature in the field is
currently focused on opaque models, the use of RL in high-stakes scenarios,
where interpretability is crucial, is still limited. Recently, some approaches
to interpretable RL, e.g., based on Decision Trees, have been proposed, but one
of the main limitations of these techniques is their training cost. To overcome
this limitation, we propose a new population-based method, called Social
Interpretable RL (SIRL), inspired by social learning principles, to improve
learning efficiency. Our method mimics a social learning process, where each
agent in a group learns to solve a given task based both on its own individual
experience as well as the experience acquired together with its peers. Our
approach is divided into two phases. In the \emph{collaborative phase}, all the
agents in the population interact with a shared instance of the environment,
where each agent observes the state and independently proposes an action. Then,
voting is performed to choose the action that will actually be performed in the
environment. In the \emph{individual phase}, each agent refines its individual
performance by interacting with its own instance of the environment. This
mechanism makes the agents experience a larger number of episodes while
simultaneously reducing the computational cost of the process. Our results on
six well-known benchmarks show that SIRL reaches state-of-the-art performance
w.r.t. the alternative interpretable methods from the literature.