Teerapat Rojanaarpa

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We present a density-based Data Pruning method for Deep Reinforcement Learning (DRL) to improve learning stability and long-term memory in rare situations. The method controls density distribution in the experience pool by discarding high correlation data and preserving rare and unique data. We apply our method to Deep Q-networks (DQN) and Deep(More)
This paper proposes a road surface recognition system based on a “laser radar” (LIDER), which is used to detect a lane markings for application to an automatic platooning system for trucks. To ensure the safety of automatic driving, there is a need to recognize the road surface conditions (dry, wet, etc.). This system proposes an integrated(More)
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