The More I Tuned My Reward Function, The Worse My RL Agent Got
A high school student encountered issues while training a reinforcement learning agent for drone navigation. The agent, designed to reach a goal while avoiding obstacles, became overly cautious and indecisive due to an overly complex reward function. By simplifying the reward to focus only on reaching the goal, progress towards it, and collision penalties, the agent's performance significantly improved. AI
IMPACT Highlights the critical role of reward function design in reinforcement learning, suggesting simpler, less prescriptive rewards can lead to better agent performance.