Reinforcement learning (RL) has shown significant potential for dealing with complex decision-making problems. However, its performance relies heavily on the availability of a large amount of high-quality data. In many real-world situations, data distribution in the target domain may differ significantly from that in the source domain, leading to a significant drop in the performance of RL algorithms. Domain adaptation (DA) strategies have been proposed to address this issue by transferring knowledge from a source domain to a target domain. However, there have been no comprehensive and in-depth studies to evaluate these approaches. In this paper we present a comprehensive and systematic study of DA in RL. We first introduce the basic concepts and formulations of DA in RL and then review the existing DA methods used in RL. Our main objective is to fill the existing literature gap regarding DA in RL. To achieve this, we conduct a rigorous evaluation of state-of-the-art DA approaches. We aim to provide comprehensive insights into DA in RL and contribute to advancing knowledge in this field. The existing DA approaches are divided into seven categories based on application domains. The approaches in each category are discussed based on the important data adaptation metrics, and then their key characteristics are described. Finally, challenging issues and future research trends are highlighted to assist researchers in developing innovative improvements.

The Decision tree algorithm is a very popular classifier for reasoning through recursive partitioning of the data space. To choose the best attributes for splitting, the range of each continuous attribute should be split into two or more intervals. Then partitioning criteria are calculated for each value. Fuzzy partitioning can be used to reduce sensitivity to noise and increase tree stability. Also, tree-building algorithms face memory limitations as they need to keep the entire training dataset in the main memory. In this paper, we introduced a fuzzy decision tree approach based on fuzzy sets. To avoid storing the entire training dataset in the main memory and overcome the memory limitations, the algorithm incrementally builds FDTs. Membership functions are automatically generated. The Fuzzy Information Gain (FIG) is then used as the fast split attribute selection criterion, and leaf expansion is performed only on the instances stored in it. The efficiency of this algorithm is examined in terms of accuracy and tree complexity. The results show that the proposed algorithm can overcome memory limitations and balance accuracy and complexity while reducing the complexity of the tree.