  Genetic algorithms have been widely used in many practical optimization
problems. Inspired by natural selection, operators, including mutation,
crossover and selection, provide effective heuristics for search and black-box
optimization. However, they have not been shown useful for deep reinforcement
learning, possibly due to the catastrophic consequence of parameter crossovers
of neural networks. Here, we present Genetic Policy Optimization (GPO), a new
genetic algorithm for sample-efficient deep policy optimization. GPO uses
imitation learning for policy crossover in the state space and applies policy
gradient methods for mutation. Our experiments on MuJoCo tasks show that GPO as
a genetic algorithm is able to provide superior performance over the
state-of-the-art policy gradient methods and achieves comparable or higher
sample efficiency.
