(IEEE, 2022-09-08) Esarte, Javier; Folino, Pablo Daniel; Gómez, Juan Carlos
Reinforcement learning is a field of artificial
intelligence that is continuously evolving and has a wide variety
of applications. In recent years major progress has been made
in the application of deep reinforcement learning to highdimensional problems with continuous state and action spaces.
This paper presents a complete analysis of the application of the
soft actor-critic algorithm to teach a four legged robot with three
joints on each leg how to move towards the center of a virtually
simulated environment. The general formulation of the
reinforcement learning problem is first presented, followed by
the description of the environment under analysis and the
applied algorithm. Afterwards, the obtained results are
compared against those of a manually programmed policy,
closing with a discussion of some key design choices and
common challenges.
