Comparison of Deep Q-Learning, Q-Learning and SARSA Reinforced Learning for Robot Local Navigation

This paper presents a performance comparison of mobile robot obstacle avoidance between using Deep Reinforcement Learning (DRL) and two classical Reinforcement Learning (RL). For the DRL-based method, Deep Q-Learning (DQN) algorithm was used whereas for the RL-based method, Q-Learning and Sarsa algorithms were used. In our experiments, we have used the extended OpenAl Gym ToolKit to compare the performances of DQN, Q-Learning, and Sarsa algorithms in both simulated and real-world environments. Turtlebot3 Burger was used as the mobile robot hardware to evaluate the performance of the RL models in the real-world environment. The average rewards, episode steps, and rate of successful navigation were used to compare the performance of the navigation ability of the RL agents. Based on the simulated and real-world results, DQN has performed significantly better than both Q-Learning and Sarsa. It has achieved 100% success rates during the simulated and real-world tests.