  Advances in Reinforcement Learning (RL) have demonstrated data efficiency and
optimal control over large state spaces at the cost of scalable performance.
Genetic methods, on the other hand, provide scalability but depict
hyperparameter sensitivity towards evolutionary operations. However, a
combination of the two methods has recently demonstrated success in scaling RL
agents to high-dimensional action spaces. Parallel to recent developments, we
present the Evolution-based Soft Actor-Critic (ESAC), a scalable RL algorithm.
We abstract exploration from exploitation by combining Evolution Strategies
(ES) with Soft Actor-Critic (SAC). Through this lens, we enable dominant skill
transfer between offsprings by making use of soft winner selections and genetic
crossovers in hindsight and simultaneously improve hyperparameter sensitivity
in evolutions using the novel Automatic Mutation Tuning (AMT). AMT gradually
replaces the entropy framework of SAC allowing the population to succeed at the
task while acting as randomly as possible, without making use of
backpropagation updates. In a study of challenging locomotion tasks consisting
of high-dimensional action spaces and sparse rewards, ESAC demonstrates
improved performance and sample efficiency in comparison to the Maximum Entropy
framework. Additionally, ESAC presents efficacious use of hardware resources
and algorithm overhead. A complete implementation of ESAC can be found at
karush17.github.io/esac-web/.
