Robot path planning using deep reinforcement learning
This work addresses autonomous navigation for mobile robots, but it is incremental as it applies existing methods to a specific domain.
The paper tackled robot path planning in unknown environments by implementing deep reinforcement learning agents (D3QN and rainbow algorithms) for obstacle avoidance and goal-oriented navigation, with results evaluated in simulation and analysis of reward function modifications.
Autonomous navigation is challenging for mobile robots, especially in an unknown environment. Commonly, the robot requires multiple sensors to map the environment, locate itself, and make a plan to reach the target. However, reinforcement learning methods offer an alternative to map-free navigation tasks by learning the optimal actions to take. In this article, deep reinforcement learning agents are implemented using variants of the deep Q networks method, the D3QN and rainbow algorithms, for both the obstacle avoidance and the goal-oriented navigation task. The agents are trained and evaluated in a simulated environment. Furthermore, an analysis of the changes in the behaviour and performance of the agents caused by modifications in the reward function is conducted.