  An important goal in reinforcement learning is to create agents that can
quickly adapt to new goals while avoiding situations that might cause damage to
themselves or their environments. One way agents learn is through exploration
mechanisms, which are needed to discover new policies. However, in deep
reinforcement learning, exploration is normally done by injecting noise in the
action space. While performing well in many domains, this setup has the
inherent risk that the noisy actions performed by the agent lead to unsafe
states in the environment. Here we introduce a novel approach called
Meta-Learned Instinctual Networks (MLIN) that allows agents to safely learn
during their lifetime while avoiding potentially hazardous states. At the core
of the approach is a plastic network trained through reinforcement learning and
an evolved "instinctual" network, which does not change during the agent's
lifetime but can modulate the noisy output of the plastic network. We test our
idea on a simple 2D navigation task with no-go zones, in which the agent has to
learn to approach new targets during deployment. MLIN outperforms standard
meta-trained networks and allows agents to learn to navigate to new targets
without colliding with any of the no-go zones. These results suggest that
meta-learning augmented with an instinctual network is a promising new approach
for safe AI, which may enable progress in this area on a variety of different
domains.
