Selected Reinforcement Learning Methods Applied to Determine the Optimal Path of Transition

The focus of this work is the determination of the optimal path for a mobile agent to take in an environment with static obstacles, using reinforcement learning (RL). The paper explains the work examining different RL algorithms, such as Q-learning and Sarsa in the classic version and enhanced with the Adam gradient optimiser. The work investigates the impact of the Adam gradient optimiser on the rate and stability of finding the optimal solution. The analysis includes a comparison of the learning rate, the number of steps in a single episode and the stability of the learning process. The results reveal that the considered Q-learning and Sarsa algorithms supplemented with the Adam optimiser achieve a higher performance, characterised by a faster determination of the optimal transition path, than the same algorithms without the Adam gradient optimiser. The results could be particularly useful in practical applications for routing transitions in fields like mobile robotics.