  The adaptive learning capabilities seen in biological neural networks are
largely a product of the self-modifying behavior emerging from online plastic
changes in synaptic connectivity. Current methods in Reinforcement Learning
(RL) only adjust to new interactions after reflection over a specified time
interval, preventing the emergence of online adaptivity. Recent work addressing
this by endowing artificial neural networks with neuromodulated plasticity have
been shown to improve performance on simple RL tasks trained using
backpropagation, but have yet to scale up to larger problems. Here we study the
problem of meta-learning in a challenging quadruped domain, where each leg of
the quadruped has a chance of becoming unusable, requiring the agent to adapt
by continuing locomotion with the remaining limbs. Results demonstrate that
agents evolved using self-modifying plastic networks are more capable of
adapting to complex meta-learning learning tasks, even outperforming the same
network updated using gradient-based algorithms while taking less time to
train.
