  We introduce two approaches for combining neural evolution strategy (NES) and
proximal policy optimization (PPO): parameter transfer and parameter space
noise. Parameter transfer is a PPO agent with parameters transferred from a NES
agent. Parameter space noise is to directly add noise to the PPO agent`s
parameters. We demonstrate that PPO could benefit from both methods through
experimental comparison on discrete action environments as well as continuous
control tasks
