Locomotion Control Framework for Snake-like Robot using Deep Reinforcement Learning
|© 2021 by IJCTT Journal|
|Year of Publication : 2021|
|Authors : Obe Olumide O, Ayogu Thomas O|
|DOI : 10.14445/22312803/IJCTT-V69I7P103|
How to Cite?
Obe Olumide O, Ayogu Thomas O, "Locomotion Control Framework for Snake-like Robot using Deep Reinforcement Learning," International Journal of Computer Trends and Technology, vol. 69, no. 7, pp. 24-28, 2021. Crossref, https://doi.org/10.14445/22312803/IJCTT-V69I7P103
In many industries today around the globe, robots can be seen carrying out different tasks. These robots have the capabilities to lift heavy loads, move at a very unbelievable speed, and execute tasks at a high level of pinpoint accuracy. But despite their amazing repertoire of tasks, most robots will find it very difficult to adapt themselves to new and environments that are unfamiliar to them. This could be because human environments are so dynamic and unpredictable and very difficult to be programmed, but rather must be learned firsthand by the robot. The desire to build machines that learn behavior based on the environment presented to them is one of the goals of Reinforcement Learning (RL). Reinforcement learning, an aspect of machine learning which is inspired by behavioral psychology, allows an agent – the learner and decision-maker, to automatically and autonomously discover optimal behavior through trial and error interactions with its environments in an attempt to solve problems. We present in this paper a control framework for Snake-like robot locomotion based on Deep Reinforcement Learning.
Locomotion, Snake-like robot, Framework, Reinforcement learning, Controller.
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