## 1 Introduction
Thus, it has the obvious disadvantage in that the less complex non-differentiable channel still takes too much time, and the result is an approximation, which is still different from the actual channel. In refs. [8; 23], a primitive AE model without a channel is proposed. Its purpose is to change the backward propagation from joint optimization to separate optimization. The training method is based on separating autoencoder from a whole to transmitter and receiver apart, using reinforcement learning to train the transmitter, and supervised learning to train the receiver. Compared with joint optimization, separate optimization often leads to training convergence by more training samples [24] due to non-simultaneous optimization. As an introduction to the development of AE, the paper mentions the corresponding development of GAN, but GAN is not the solution to reduce the computation complexity. Most of the GANs are committed to be trained from fiber channels that require high precision and low complexity [25]. Model collapse is the main problem in GAN. Model collapse comes from not considering the diversity of the data. GAN model training affects whether the final result converges, so different loss functions and condition vector designs are needed for different channels to solve the problem of modal collapse. In this paper, compared to Reference [15], we implement the improved end-to-end optical fiber system based on reinforcement learning and supervised learning. We realized the limitations of the (Feed Forward Neural Network) FFNN design for communication with blocks over nonlinear channels and improve end-to-end optical fiber system by implementing a reinforcement learning algorithm as a receiver, without the single block but the upsampling block, to receive every message. We designed the system to train an autoencoder by simulation channel jointly, and optical fiber channel separately. In consideration of the complexity and BER, we broke away from the field of supervised learning and focused on reinforcement learning. Reinforcement learning is a reward guiding behavior that agents learn in the way of \"trial and error\" and interact with the environment. The goal is to maximize the reward for agents. Compared with supervised learning, reinforcement learning is more sensitive to the training environment and is more conducive to serving as a receiver to distinguish different information. As a representative of Reinforcement Learning, the Adaboost algorithm [26] has been demonstrated as an addition with many weighted weak classifiers. Its advantage is low complexity and easy training. In addition, multiple weak classifiers include various signal misjudgments caused by pulse broadening, which further reduces the bit error rate through training. In ref. [12], this paper aims to find that AdaBoost has a relatively good effect on classification. Due to training separately, the receiver based on the Adaboost algorithm still has good performance received from an equally-trained transmitter under a different-distance optical fiber channel. We use the Adaboost algorithm as the receiver to design an end-to-end optimized fiber-optic system receiver to overcome inter-symbol interference (ISI) present in Intensity Modulation/Direct Detection (IM/DD) over channels with CD. Since we receive all the block (carry the same message) to solve the same message, pulse broadening resulted by CD between the block cannot influence the message any longer. Based on our previous research [27], we further analyzed the advantages and disadvantages of the Adaboost algorithm, other Machine Learning algorithms, and ANN, and we performed a more detailed analysis of the experimental details, with more comparison. Experimental results show that the receiver with the Adaboost algorithm can outperform the previous end-to-end FFNN and other Machine Learning algorithms in complexity and efficiency.