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Multi-Agent Credit Assignment in Stochastic Resource Management Games
(2017-08)
Multi-Agent Systems (MAS) are a form of distributed intelligence, where multiple autonomous
agents act in a common environment. Numerous complex, real world systems have been
successfully optimised using Multi-Agent ...
Vulnerable road user detection: state-of-the-art and open challenges
(2019)
Correctly identifying vulnerable road users (VRUs), e.g. cyclists and pedestrians, remains one of the most challenging environment perception tasks for autonomous vehicles (AVs). This work surveys the current state-of-the-art ...
Potential-Based Reward Shaping Preserves Pareto Optimal Policies
(2017-05)
Reward shaping is a well-established family of techniques
that have been successfully used to improve the performance
and learning speed of Reinforcement Learning agents in singleobjective
problems. Here we extend the ...
Policy Invariance under Reward Transformations for Multi-Objective Reinforcement Learning
(2017)
Reinforcement Learning (RL) is a powerful and well-studied Machine Learning
paradigm, where an agent learns to improve its performance in an environment
by maximising a reward signal. In multi-objective Reinforcement
Learning ...
Curriculum Learning for Tightly Coupled Multiagent Systems
(2019)
In this paper,we leverage curriculum learning(CL) to improve the performance of multiagent systems(MAS) that are trained
with the cooperative coevolution of artificial neural networks. We design curricula to progressively ...