A self?adaptive SAC?PID control approach based on reinforcement learning for mobile robots

A reinforcement learning approach to obstacle avoidance of mobile robots

One of the basic issues in navigation of autonomous mobile robots is the obstacle avoidance task that is commonly achieved using reactive control paradigm where a local mapping from perceived states to actions is acquired. A control strategy with learning capabilities in an unknown environment can be obtained using reinforcement learning where the learning agent is given only sparse reward info...

Effective Reinforcement Learning for Mobile Robots

Programming mobile robots can be a long, time-consuming process. Specifying the low-level mapping from sensors to actuators is prone to programmer misconceptions, and debugging such a mapping can be tedious. The idea of having a robot learn how to accomplish a task, rather than being told explicitly is an appealing one. It seems easier and much more intuitive for the programmer to specify what ...

Mobile robots exploration through cnn-based reinforcement learning

Exploration in an unknown environment is an elemental application for mobile robots. In this paper, we outlined a reinforcement learning method aiming for solving the exploration problem in a corridor environment. The learning model took the depth image from an RGB-D sensor as the only input. The feature representation of the depth image was extracted through a pre-trained convolutional-neural-...

Decentralized Reinforcement Learning Applied to Mobile Robots

In this paper, decentralized reinforcement learning is applied to a control problem with a multidimensional action space. We propose a decentralized reinforcement learning architecture for a mobile robot, where the individual components of the commanded velocity vector are learned in parallel by separate agents. We empirically demonstrate that the decentralized architecture outperforms its cent...

Motion Control for Fast Mobile Robots: A Trajectory-Based Approach