Model-based reinforcement learning experimental study for mobile robot navigation

This paper presents experimental results of mobile robot navigation using two predictive controllers – a conventional model-predictive control and a Q-learning predictive controller. The latter essentially substitutes the running objective roll-outs with predicted action-value (Q-function) estimates. The idea behind such an approach is to integrate capabilities of reinforcement learning agents into the setting of model-predictive control while retaining the safety guarantees of the latter. Noteworthy the action sequence calculation step in both algorithms is of the same computational complexity. Yet, as we observed in our experiments, the learning predictive controller was able to outperform the model-predictive baseline. The code for the environment simulation may be found under https://github.com/thd-research/RL-autonomous-navigation.