## 2 Motivation and Related Work  
Our main motivation is to validate the possibilities of developing fast-learning and more robust RL agents using environment augmentation with GANs. While there has been work concerning improving the performance of RL agents and GANs, the feasibility of environment augmentation via GANs seems to be slightly unexplored. [PERSON] et al. [2] proposed the enhanced training of RL agents by using EGANs to initialize the agents in order to make them learn faster. The EGAN approach could be used to speed up the early training phases for real-time systems with sparse and slow data sampling. Consequently, agents trained with EGAN and GAN achieved faster increases in rewards, and both could provide more modalities in data space. We identified that suitability is only mentioned for wireless communication. Exploration is required for applying the technique to other domains. Further work is needed to verify and fine-tune the system to achieve an optimal performance. [PERSON] et al. [3] proposed using the GAN environment for GAN-Q-learning to analyze its performance and provide a novel distributional RL method. This is an alternative to the traditional models. The end results state that Q-learning goes along with the benchmarked algorithms, such as GAN-Q-learning. A CNN was suggested in order to use rewards, and a high priority for the stability of future iterations should be carried out. [PERSON] et al. [4] provided many training techniques that generate realistic human images. The methods provided can train RL agents, which were previously untrainable. Using the methods proposed, we can use them for training RL agents, reducing the training  
Figure 1: Working of GANs. time. [PERSON], [PERSON] et al. [5] presented their effort to synthesize pulmonary sounds of various categories utilizing variations in variational autoencoders, such as multi-layer perceptron VAE (MLP-VAE), convolutional VAE (CVAE) and conditional VAE. They compared the impact of supplementing the unbalanced dataset on the performance of different lung sound classifiers. To identify and categorize dementia into different groups based on its prominence and severity in the available MRI images, [PERSON] et al. [6] suggested a unique DCGAN-based augmentation and classification (D-BAC) model technique. They suggested using a new GAN-augmented dataset to study the early detection of dementia, also known as mild cognitive impairment (MCI) and referred to as MCI. Machine learning, especially reinforcement learning, has recently found widespread use in wireless communications in the prediction of network traffic, identifying the quickest and shortest path while sending the data over the networks [7; 8]. The model-free methodology known as Q-learning has been acknowledged as promising, mainly when used for complex decision-making processes. Excellent learning capabilities, successful outcomes and the ability to integrate with other models are just a few advantages of Q-learning [9]. According to [PERSON] et al., DeepCQ+ routing [10], which innovatively integrates emerging multi-agent deep reinforcement learning (MADRL), achieves a consistently higher performance across various MANET architectures when training on only a small number of network parameters and conditions. [PERSON] et al. created a novel packet routing architecture based on multi-agent deep reinforcement learning in a completely decentralized setting. Each router in this system is equipped with a recurrent neural network (RNN) for training and decision making and an independent long short-term memory (LSTM) [11]. The Q-values associated with each potential course of action for a given input state serve as the targets of an input state in deep Q-learning. Q-values are the current estimates of the total future rewards obtained for each activity. These Q-values are determined via the temporal difference formula [12]. [PERSON] et al.