Abstract
To improve the efficiency of deep reinforcement learning (DRL)-based methods for robotic trajectory planning in the unstructured working environment with obstacles. Different from the traditional sparse reward function, this paper presents two brand-new dense reward functions. First, the azimuth reward function is proposed to accelerate the learning process locally with a more reasonable trajectory by modeling the position and orientation constraints, which can reduce the blindness of exploration dramatically. To further improve the efficiency, a reward function at subtask-level is proposed to provide global guidance for the agent in the DRL. The subtask-level reward function is designed under the assumption that the task can be divided into several subtasks, which reduces the invalid exploration greatly. The extensive experiments show that the proposed reward functions are able to improve the convergence rate by up to three times with the state-of-the-art DRL methods. The percentage increase in convergence means is 2.25%-13.22% and the percentage decreases with respect to standard deviation by 10.8%-74.5%.
Highlights
Trajectory planning is a fundamental problem for the motion control of robot manipulator
Dense reward function give more information after each action, which can reduce the blindness of exploration of Deep Reinforcement Learning (DRL) methods in trajectory planning task
SUBTASK-LEVEL REWARD FUNCTION the proposed azimuth reward function in Section 2 is able to reduce the local blindness of exploration using DRL methods, it mainly focuses on the local exploration at one moment, lacks of global guidance in trajectory planning task
Summary
Trajectory planning is a fundamental problem for the motion control of robot manipulator. It enables the robot manipulator to autonomously learn and plan an optimal trajectory in unstructured working environment. Xie et al.: Deep Reinforcement Learning With Optimized Reward Functions for Robotic Trajectory Planning are discrete, which cannot be applied to the tasks with continuous action spaces, just like trajectory planning of robot manipulator To solve this problem, Deep Deterministic Strategy Gradient (DDPG) [19] and Critics of Asynchronous Advantage Actors (A3C) [20] are put forward. The primary contributions of this paper are summarized as follows: 1) Considering the features of trajectory planning task and work environment, two brand-new dense reward functions are proposed. Dense reward function give more information after each action, which can reduce the blindness of exploration of DRL methods in trajectory planning task.
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