  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.
