Mathematics

Reinforcement learning

Hyperparameter optimization

Support vector machine

Machine learning

Heuristic

Political science

Artificial intelligence

Economics

Grid

Programming language

Problem statement

Geometry

Statement (logic)

Path (computing)

Computer science

Management science

Robotics

Artificial neural network

Motion planning

Robot

Procedia Computer Science

Single-shot grid-based path finding is an important problem with the applications in robotics, video games etc. Typically in AI community heuristic search methods (based on A* and its variations) are used to solve it. In this work we present the results of preliminary studies on how neural networks can be utilized to path planning on square grids, e.g. how well they can cope with path finding tasks by themselves within the well-known reinforcement problem statement. Conducted experiments show that the agent using neural Q-learning algorithm robustly learns to achieve the goal on small maps and demonstrate promising results on the maps have ben never seen by him before.

Grid Path Planning with Deep Reinforcement Learning: Preliminary Results