Improving RL Speed by Adding Unseen Experiences via Operators Inspired by Genetic Algorithm Operators Enriched by Chaotic Random Generator

In many Multi-Agent Systems, under-education agents investigate their environments to discover their target(s). Any agent can also learn its strategy. In multi-task learning, one agent studies a set of related problems together simultaneously, by a common model. In reinforcement learning exploration phase, it is necessary to introduce a process of trial and error to learn better rewards obtained from environment. To reach this end, anyone can typically employ the uniform pseudorandom number generator in exploration period. On the other hand, it is predictable that chaotic sources also offer a random-like series comparable to stochastic ones. It is useful in multi-task reinforcement learning, to use teammate agents´ experience by doing simple interactions between each other. We employ the past experiences of agents to enhance performance of multi-task learning in a nondeterministic environment. Communications are created by operators of evolutionary algorithm. In this paper we have also employed the chaotic generator in the exploration phase of reinforcement learning in a nondeterministic maze problem. We obtained interesting results in the maze problem.