Comparison of Reinforcement Learning Activation Functions to Improve the Performance of the Racing Game Learning Agent

다국어 초록 (Multilingual Abstract)

Recently, research has been actively conducted to create artificial intelligence agents that learn games throughreinforcement learning. There are several factors that determine performance when the agent learns a game, butusing any of the activation functions is also an important factor. This paper compares and evaluates whichactivation function gets the best results if the agent learns the game through reinforcement learning in the 2Dracing game environment. We built the agent using a reinforcement learning algorithm and a neural network.
We evaluated the activation functions in the network by switching them together. We measured the reward, theoutput of the advantage function, and the output of the loss function while training and testing. As a result ofperformance evaluation, we found out the best activation function for the agent to learn the game. Thedifference between the best and the worst was 35.4%.

참고문헌 (Reference)

1 G. C. Tripathi, "Swish activation based deep neural network predistorter for RFPA" 1239-1242, 2019

2 Z. Huang, "SNDCNN: self-normalizing deep CNNs with scaled exponential linear units for speech recognition"

3 G. Brockman, "OpenAI gym"

4 M. N. Moghadasi, "Evaluating Markov decision process as a model for decision making under uncertainty environment" 2446-2450, 2007

5 Z. Wang, "Efficient deep convolutional neural networks using CReLU for ATR with limited SAR images" 2019 (2019): 7615-7618, 2019

6 L. Lu, "Dying ReLU and initialization: theory and numerical examples"

7 X. Zhang, "Dilated convolution neural network with LeakyReLU for environmental sound classification" 1-5, 2017

8 A. Shah, "Deep residual networks with exponential linear unit" 59-65, 2016

9 R. Yamashita, "Convolutional neural networks : an overview and application in radiology" 9 (9): 611-629, 2018

10 A. Jeerige, "Comparison of deep reinforcement learning approaches for intelligent game playing" 366-371, 2019

11 D. W. Lu, "Agent inspired trading using recurrent reinforcement learning and LSTM neural networks"

12 이동철, "2D 슈팅 게임 학습 에이전트의 성능 향상을 위한 딥러닝 활성화 함수 비교 분석" 한국인터넷방송통신학회 19 (19): 135-141, 2019