Title: Policy Optimization by Genetic Distillation . Abstract: 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.