  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.
