A Dynamic Adjusting Reward Function Method for Deep Reinforcement Learning with Adjustable Parameters

In deep reinforcement learning, network convergence speed is often slow and easily converges to local optimal solutions. For an environment with reward saltation, we propose a magnify saltatory reward (MSR) algorithm with variable parameters from the perspective of sample usage. MSR dynamically adjusts the rewards for experience with reward saltation in the experience pool, thereby increasing an agent's utilization of these experiences. We conducted experiments in a simulated obstacle avoidance search environment of an unmanned aerial vehicle and compared the experimental results of deep Q-network (DQN), double DQN, and dueling DQN after adding MSR. The experimental results demonstrate that, after adding MSR, the algorithms exhibit a faster network convergence and can obtain the global optimal solution easily.