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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 ...
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 ...
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 ...