A Reinforcement Learning based Path Planning Approach in 3D Environment

TL;DR

This paper compares reinforcement learning methods for 3D path planning, highlighting the trade-off between stability and computational efficiency in deterministic and stochastic approaches.

Contribution

It provides a comparative analysis of deterministic tree-based and Q-learning approaches for 3D path planning in dynamic environments.

Findings

Deterministic tree search yields highly stable paths.

Q-learning and policy gradient are faster but less stable.

Trade-offs between accuracy and computation time are demonstrated.

Abstract

Optimal motion planning involves obstacles avoidance where path planning is the key to success in optimal motion planning. Due to the computational demands, most of the path planning algorithms can not be employed for real-time based applications. Model-based reinforcement learning approaches for path planning have received certain success in the recent past. Yet, most of such approaches do not have deterministic output due to the randomness. We analyzed several types of reinforcement learning-based approaches for path planning. One of them is a deterministic tree-based approach and other two approaches are based on Q-learning and approximate policy gradient, respectively. We tested preceding approaches on two different simulators, each of which consists of a set of random obstacles that can be changed or moved dynamically. After analysing the result and computation time, we concluded that the deterministic tree search approach provides highly stable result. However, the computational time is considerably higher than the other two approaches. Finally, the comparative results are provided in terms of accuracy and computational time as evidence.