  Peer learning is a novel high-level reinforcement learning framework for
agents learning in groups. While standard reinforcement learning trains an
individual agent in trial-and-error fashion, all on its own, peer learning
addresses a related setting in which a group of agents, i.e., peers, learns to
master a task simultaneously together from scratch. Peers are allowed to
communicate only about their own states and actions recommended by others:
"What would you do in my situation?". Our motivation is to study the learning
behavior of these agents. We formalize the teacher selection process in the
action advice setting as a multi-armed bandit problem and therefore highlight
the need for exploration. Eventually, we analyze the learning behavior of the
peers and observe their ability to rank the agents' performance within the
study group and understand which agents give reliable advice. Further, we
compare peer learning with single agent learning and a state-of-the-art action
advice baseline. We show that peer learning is able to outperform single-agent
learning and the baseline in several challenging discrete and continuous OpenAI
Gym domains. Doing so, we also show that within such a framework complex
policies from action recommendations beyond discrete action spaces can evolve.
