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Exploring applications of deep reinforcement learning for real-world autonomous driving systems
(2019-01)
Deep Reinforcement Learning (DRL) has become increasingly powerful in recent years, with notable achievements
such as Deepmind’s AlphaGo. It has been successfully deployed in commercial vehicles like Mobileye’s
path ...
Analysing the Effects of Reward Shaping in Multi-Objective Stochastic Games
(2017-05)
The majority ofMulti-Agent Reinforcement Learning (MARL)
implementations aim to optimise systems with respect to a
single objective, despite the fact that many real world problems
are inherently multi-objective in nature. ...
A Theoretical and Empirical Analysis of Reward Transformations in Multi-Objective Stochastic Games
(2017)
Reward shaping has been proposed as a means to address the
credit assignment problem in Multi-Agent Systems (MAS).
Two popular shaping methods are Potential-Based Reward
Shaping and di erence rewards, and both have been ...