Policy Optimization by Genetic Distillation
This work addresses the problem of sample-efficient policy optimization in deep reinforcement learning for researchers and practitioners, offering a novel genetic approach that is not incremental but introduces a new method.
The paper tackles the challenge of applying genetic algorithms to deep reinforcement learning by introducing Genetic Policy Optimization (GPO), which uses imitation learning for policy crossover and policy gradient methods for mutation, achieving superior performance and comparable or higher sample efficiency over state-of-the-art methods on MuJoCo tasks.
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.