Genetic Deep Reinforcement Learning for Mapless Navigation

摘要

We consider Deep Reinforcement Learning (DRL) approaches to devise mapless navigation strategies for mobile platforms. We propose a Genetic Deep Reinforcement Learning (GDRL) method that combines Genetic Algorithms (GA) with discrete and continuous action space DRL approaches. The goal of GDRL is to reduce the sensitivity of DRL approaches to their hyper-parameter tuning and to provide robust exploration strategies. We evaluate GDRL in combination with Rainbow and Proximal Policy Optimization (PPO) in two navigation scenarios: i) a wheeled robot avoiding obstacles in an indoor environment and ii) a water drone that must reach a predefined location in presence of waves. Our empirical evaluation demonstrates that GDRL outperforms state-of-the-art DRL and GA methods as well as a previous hybrid approach.