Mapless Navigation with Deep Reinforcement Learning in Indoor Environment

Abstract

One of the crucial tasks for autonomous robots is learning to safely navigate through obstacles in real-world environments. An intelligent robot must not only perform the assigned task but also adapt to changes in its environment as quickly as possible. In this work, we propose an improved version of the Deep Reinforcement Learning (DRL) Proximal Policy Optimization (PPO) algorithm by modifying a deep neural network of the Actor and Critic. Then we compare the results of our work by comparing them with those of classical PPO. Algorithm testing is conducted in a Flatland simulation environment, which allows for integration with the ROS2 operating environment.