  As reinforcement learning agents become increasingly integrated into complex,
real-world environments, designing for safety becomes a critical consideration.
We specifically focus on researching scenarios where agents can cause undesired
side effects while executing a policy on a primary task. Since one can define
multiple tasks for a given environment dynamics, there are two important
challenges. First, we need to abstract the concept of safety that applies
broadly to that environment independent of the specific task being executed.
Second, we need a mechanism for the abstracted notion of safety to modulate the
actions of agents executing different policies to minimize their side-effects.
In this work, we propose Safety Aware Reinforcement Learning (SARL) - a
framework where a virtual safe agent modulates the actions of a main
reward-based agent to minimize side effects. The safe agent learns a
task-independent notion of safety for a given environment. The main agent is
then trained with a regularization loss given by the distance between the
native action probabilities of the two agents. Since the safe agent effectively
abstracts a task-independent notion of safety via its action probabilities, it
can be ported to modulate multiple policies solving different tasks within the
given environment without further training. We contrast this with solutions
that rely on task-specific regularization metrics and test our framework on the
SafeLife Suite, based on Conway's Game of Life, comprising a number of complex
tasks in dynamic environments. We show that our solution is able to match the
performance of solutions that rely on task-specific side-effect penalties on
both the primary and safety objectives while additionally providing the benefit
of generalizability and portability.
