Obstacle Avoidance and Navigation Utilizing Reinforcement Learning with Reward Shaping
This addresses navigation challenges for robotics, but it is incremental as it builds on existing methods.
The authors tackled robotic obstacle avoidance and navigation by revising DDPG and PPO algorithms with improved reward shaping, achieving better results in simulations with a real mobile robot.
In this paper, we investigate the obstacle avoidance and navigation problem in the robotic control area. For solving such a problem, we propose revised Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization algorithms with an improved reward shaping technique. We compare the performances between the original DDPG and PPO with the revised version of both on simulations with a real mobile robot and demonstrate that the proposed algorithms achieve better results.