  This work investigates the potential of Reinforcement Learning (RL) to tackle
robot motion planning challenges in the dynamic RoboCup Small Size League
(SSL). Using a heuristic control approach, we evaluate RL's effectiveness in
obstacle-free and single-obstacle path-planning environments. Ablation studies
reveal significant performance improvements. Our method achieved a 60% time
gain in obstacle-free environments compared to baseline algorithms.
Additionally, our findings demonstrated dynamic obstacle avoidance
capabilities, adeptly navigating around moving blocks. These findings highlight
the potential of RL to enhance robot motion planning in the challenging and
unpredictable SSL environment.
