Deep reinforcement learning technique combines reinforcement learning and neural network for various applications. This paper is to propose an effective lazy training method for deep reinforcement learning, especially for deep Qnetwork combining neural network with Q-learning to be used for the obstacle avoidance and path planning applications. The proposed method can reduce the overall training time by designing a lazy learning method and a method removing unnecessary repetitions in the training step. These two methods can reduce a significant portion of total execution time without losing any required accuracy. The proposed method is evaluated for the obstacle avoidance and path planning tasks, where an agent trapped in an unknown environment is trying to find out the shortest path to the destination without any collision, through its self-study. And the experiment results show that the proposed method reduces 53.38% of training time on average, compared to the traditional method with no performance loss and make the training procedure more stable.
|Title of host publication||2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2017 Nov 27|
|Event||2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 - Banff, Canada|
Duration: 2017 Oct 5 → 2017 Oct 8
|Name||2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017|
|Other||2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017|
|Period||17/10/5 → 17/10/8|
Bibliographical noteFunding Information:
This work was partially supported by Institute for Information and Communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (R0124-16-0002, Emotional Intelligence Technology to Infer Human Emotion and Carry on Dialogue Accordingly) and the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2015R1A2A2A01007668).
© 2017 IEEE.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Science Applications
- Human-Computer Interaction
- Control and Optimization