Deep Learning with Evolutionary Strategies for Building Autonomous Agents Behaviour

摘要

In this study, we will consider the construction of the behaviour of an autonomous agent in an environment that has many traps and a large number of obstacles. Such environments require the agent to build a policy that will lead them to the goal as quickly as possible. As a working basis, we use Reinforcement Learning and apply approaches from the field of random search and Evolutionary Strategies.