Abstract:
In many industries today around the globe, robots can be seen carrying out different tasks. These
robots have capabilities to lift heavy loads, move at a very unbelievable speed, and execute tasks
at a high level of pin-point accuracy. But despite this 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. This work developed a Deep Reinforcement
Learning based locomotion control model for snake-like robot. The agent proved to be robust for
snake-like robot locomotion in training and achieved 95.8% successful episodes in the evaluation
scenario. The learned locomotion by the agent is remarkably similar to the slithering gait and
performs a similar sinus-like movement of a real snake. Deep Reinforcement Learning approach
therefore remains promising for future application with snake-like robot.
