On Avoidance Learning with Partial Observability
  We study a framework where agents have to avoid aversive signals. The agents
are given only partial information, in the form of features that are
projections of task states. Additionally, the agents have to cope with
non-determinism, defined as unpredictability on the way that actions are
executed. The goal of each agent is to define its behavior based on
feature-action pairs that reliably avoid aversive signals. We study a learning
algorithm, called A-learning, that exhibits fixpoint convergence, where the
belief of the allowed feature-action pairs eventually becomes fixed. A-learning
is parameter-free and easy to implement.
