Autonomous Driving: A Multi-Objective Deep Reinforcement Learning Approach

autonomous driving;reinforcement learning;Markov decision process;deep learning

Li, Changjianadvisor:Czarnecki, Krzysztof ; affiliation1:Faculty of Engineering ; Czarnecki, Krzysztof ;

University of Waterloo

关键词: deep learning;    Master Thesis;    Markov decision process;    autonomous driving;    reinforcement learning;   

Others  :  https://uwspace.uwaterloo.ca/bitstream/10012/14697/3/Li_Changjian.pdf

瑞士|英语

来源: UWSPACE Waterloo Institutional Repository

【 摘 要 】

Autonomous driving is a challenging domain that entails multiple aspects: a vehicle should be able to drive to its destination as fast as possible while avoiding collision, obeying traffic rules and ensuring the comfort of passengers. It's representative of complex reinforcement learning tasks humans encounter in real life. The aim of this thesis is to explore the effectiveness of multi-objective reinforcement learning for such tasks characterized by autonomous driving. In particular, it shows that:1. Multi-objective reinforcement learning is effective at overcoming some of the difficulties faced by scalar-reward reinforcement learning, and a multi-objective DQN agent based on a variant of thresholded lexicographic Q-learning is successfully trained to drive on multi-lane roads and intersections, yielding and changing lanes according to traffic rules.2. Data efficiency of (multi-objective) reinforcement learning can be significantly improved by exploiting the factored structure of a task. Specifically, factored Q functions learned on the factored state space can be used as features to the original Q function to speed up learning.3. Inclusion of history-dependent policies enables an intuitive exact algorithm for multi-objective reinforcement learning with thresholded lexicographic order.