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Nanyang Technological University, Singapore

School of Computer Science and Engineering(SCSE)

Final Year Project: SCE17-0434

Reinforcement Learning for Self-Driving Cars

Abstract

This project implements reinforcement learning to generate a self-driving car-agent with deep learning network to maximize its speed. The convolutional neural network was implemented to extract features from a matrix representing the environment mapping of self-driving car. The model acts as value functions for five actions estimating future rewards. The model is trained under Q-learning algorithm in a simulation built to simulate traffic condition of seven-lane expressway. After continuous training for 2340 minutes, the model learns the control policies for different traffic conditions and reaches an average speed 94 km/h compared to maximum speed of 110 km/h.

Simulator

screenshot Simulator running under macOS High Sierra environment

Requirements

  • Tensorflow
  • pygame
  • NumPy
  • PIL

Model architecture

architecture High level model architecture design

tensorflow structure Graph structure in Tensorflow

Results

Average speed

chart Average speed against number of training episode

Score

chart Score against number of training episode

Training loss

chart Loss against number of training episode

Sum of Q-values

chart Sum of Q-values against number of training episode

Evaluation

Traffic condition Description
1. Light traffic Maximum 20 cars are simulated with plenty room for overtaking.
2. Medium traffic Maximum 40 cars are simulated with lesser chance to overtake other cars.
3. Heavy traffic Maximum 60 cars are simulated to simulate heavy traffic.

chart Condition 1: Average speed against average number of emergency brake applied

chart Condition 2: Average speed against average number of emergency brake applied

chart Condition 3: Average speed against average number of emergency brake applied

Biblography

  1. Sallab, A.E., Abdou, M., Perot, E., and Yogamani, S.: ‘Deep reinforcement learning framework for autonomous driving’, Electronic Imaging, 2017, 2017, (19), pp. 70-76
  2. Sutton, R.S.: ‘Learning to predict by the methods of temporal differences’, Machine learning, 1988, 3, (1), pp. 9-44
  3. Bellemare, M.G., Veness, J., and Bowling, M.: ‘Investigating Contingency Awareness Using Atari 2600 Games’, in Editor (Ed.)^(Eds.): ‘Book Investigating Contingency Awareness Using Atari 2600 Games’ (2012, edn.), pp.
  4. Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M.: ‘Playing atari with deep reinforcement learning’, arXiv preprint arXiv:1312.5602, 2013
  5. Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L.D., Monfort, M., Muller, U., and Zhang, J.: ‘End to end learning for self-driving cars’, arXiv preprint arXiv:1604.07316, 2016
  6. Chen, C., Seff, A., Kornhauser, A., and Xiao, J.: ‘Deepdriving: Learning affordance for direct perception in autonomous driving’, in Editor (Ed.)^(Eds.): ‘Book Deepdriving: Learning affordance for direct perception in autonomous driving’ (2015, edn.), pp. 2722-2730
  7. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., and Ostrovski, G.: ‘Human-level control through deep reinforcement learning’, Nature, 2015, 518, (7540), pp. 529-533
  8. Yu, A., Palefsky-Smith, R., and Bedi, R.: ‘Deep Reinforcement Learning for Simulated Autonomous Vehicle Control’, Course Project Reports: Winter, 2016, pp. 1-7