  Agents that operate in an unknown environment are bound to make mistakes
while learning, including, at least occasionally, some that lead to
catastrophic consequences. When humans make catastrophic mistakes, they are
expected to learn never to repeat them, such as a toddler who touches a hot
stove and immediately learns never to do so again. In this work we consider a
novel class of POMDPs, called POMDP with Catastrophic Actions (POMDP-CA) in
which pairs of states and actions are labeled as catastrophic. Agents that act
in a POMDP-CA do not have a priori knowledge about which (state, action) pairs
are catastrophic, thus they are sure to make mistakes when trying to learn any
meaningful policy. Rather, their aim is to maximize reward while never
repeating mistakes.
  As a first step of avoiding mistake repetition, we leverage the concept of a
shield which prevents agents from executing specific actions from specific
states.
  In particular, we store catastrophic mistakes (unsafe pairs of states and
actions) that agents make in a database. Agents are then forbidden to pick
actions that appear in the database. This approach is especially useful in a
continual learning setting, where groups of agents perform a variety of tasks
over time in the same underlying environment. In this setting, a task-agnostic
shield can be constructed in a way that stores mistakes made by any agent, such
that once one agent in a group makes a mistake the entire group learns to never
repeat that mistake. This paper introduces a variant of the PPO algorithm that
utilizes this shield, called ShieldPPO, and empirically evaluates it in a
controlled environment.
  Results indicate that ShieldPPO outperforms PPO, as well as baseline methods
from the safe reinforcement learning literature, in a range of settings.
