Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Comparison of Reinforcement Learning Activation Functions to Improve the Performance of the Racing Game Learning Agent

  • Received : 2020.03.25
  • Accepted : 2020.07.27
  • Published : 2020.10.31
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Abstract

Recently, research has been actively conducted to create artificial intelligence agents that learn games through reinforcement learning. There are several factors that determine performance when the agent learns a game, but using any of the activation functions is also an important factor. This paper compares and evaluates which activation function gets the best results if the agent learns the game through reinforcement learning in the 2D racing 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, the output of the advantage function, and the output of the loss function while training and testing. As a result of performance evaluation, we found out the best activation function for the agent to learn the game. The difference between the best and the worst was 35.4%.

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References

  1. A. Jeerige, D. Bein, and A. Verma, "Comparison of deep reinforcement learning approaches for intelligent game playing," in Proceedings of 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, 2019, pp. 366-371.
  2. M. N. Moghadasi, A. T. Haghighat, and S. S. Ghidary, "Evaluating Markov decision process as a model for decision making under uncertainty environment," in Proceedings of 2007 International Conference on Machine Learning and Cybernetics, Hong Kong, China, 2007, pp. 2446-2450.
  3. D. Lee and B. Park, "Comparison of deep learning activation functions for performance improvement of a 2D shooting game learning agent," The Journal of the Institute of Internet, Broadcasting and Communication, vol. 19, no. 2, pp. 135-141, 2019. https://doi.org/10.7236/JIIBC.2019.19.2.135
  4. R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, "Convolutional neural networks: an overview and application in radiology," Insights into Imaging, vol. 9, no. 4, pp. 611-629, 2018. https://doi.org/10.1007/s13244-018-0639-9
  5. D. W. Lu, "Agent inspired trading using recurrent reinforcement learning and LSTM neural networks," 2017 [Online]. Available: https://arxiv.org/abs/1707.07338.
  6. G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba, "OpenAI gym," 2016 [Online]. Available: https://arxiv.org/abs/1606.01540.
  7. L. Lu, Y. Shin, Y. Su, and G. E. Karniadakis, "Dying ReLU and initialization: theory and numerical examples," 2019 [Online]. Available: https://arxiv.org/abs/1903.06733.
  8. X. Zhang, Y. Zou, and W. Shi, "Dilated convolution neural network with LeakyReLU for environmental sound classification," in Proceedings of 2017 22nd International Conference on Digital Signal Processing (DSP), London, UK, 2017, pp. 1-5.
  9. A. Shah, E. Kadam, H. Shah, S. Shinde, and S. Shingade, "Deep residual networks with exponential linear unit," in Proceedings of the 3rd International Symposium on Computer Vision and the Internet, Jaipur, India, 2016, pp. 59-65.
  10. Z. Huang, T. Ng, L. Liu, H. Mason, X. Zhuang, and D. Liu, "SNDCNN: self-normalizing deep CNNs with scaled exponential linear units for speech recognition," 2019 [Online]. Available: https://arxiv.org/abs/1910.01992.
  11. G. C. Tripathi, M. Rawat, and K. Rawat, "Swish activation based deep neural network predistorter for RF-PA," in Proceedings of 2019 IEEE Region 10 Conference (TENCON), Kochi, India, 2019, pp. 1239-1242.
  12. Z. Wang and X. Xu, "Efficient deep convolutional neural networks using CReLU for ATR with limited SAR images," The Journal of Engineering, vol. 2019, no. 21, pp. 7615-7618, 2019. https://doi.org/10.1049/joe.2019.0567