Daniel Chi Kit Ngai

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In this paper, we present a learning method to solve the vehicle overtaking problem, which demands a multitude of abilities from the agent to tackle multiple criteria. To handle this problem, we propose to adopt a multiple-goal reinforcement learning (MGRL) framework as the basis of our solution. By considering seven different goals, either Q-learning (QL)(More)
This paper describes the performance evaluation of Double-Action Q-learning in solving the moving obstacle avoidance problem. The evaluation is focused on two aspects: 1) obstacle avoidance, and 2) goal seeking; where four parameters are considered, namely, sum of rewards, no. of collisions, steps per episode, and obstacle density. Comparison is made(More)
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