Scalarized multi-objective reinforcement learning: Novel design techniques (abstract)

In multi-objective problems, it is key to find compromising solutions that balance different objectives. The linear scalarization function is often utilized to translate the multi-objective nature of a problem into a standard, single-objective problem. Generally, it is noted that such as linear combination can only find solutions in convex areas of the Pareto front, therefore making the method inapplicable in situations where the shape of the front is not known beforehand. We propose a non-linear scalarization function, called the Chebyshev scalarization function in multi-objective reinforcement learning. We show that the Chebyshev scalarization method overcomes the flaws of the linear scalarization function and is able to discover all Pareto optimal solutions in non-convex environments

Context-sensitive reward shaping for sparse interaction MAS (abstract)

This paper describes the use of context aware potential functions in a multi-agent system in which the interactions between agents are sparse to guide agents towards the desired solutions. These sparse interactions mean that the interactions between the agents only occur sporadically, unknown to the agents a priori, in certain regions of the state space. During these interactions, agents need to coordinate in order to reach the global optimal solution. We demonstrate how different reward shaping functions can be used on top of Future Coordinating Q-learning (FCQ); an algorithm capable of automatically detecting when agents should take each other into consideration. Using FCQ-learning, coordination problems can even be anticipated before the actual problems occur, allowing the problems to be solved timely. We evaluate our approach on a range of gridworld problems, as well as a simulation of Air Traffic Control

Solving Satisfiability in Fuzzy Logics by Mixing CMA-ES (abstract)

Satisfiability in propositional logic is well researched and many approaches to checking and solving exist. In infinite-valued or fuzzy logics, however, there have only recently been attempts at developing methods for solving satisfiability. In this paper, we analyse the function landscape of different problem classes, focussing our analysis on plateaus. Based on this study, we develop Mixing CMA-ES (M-CMAES), an extension to CMA-ES that is well suited to solving problems with many large plateaus. We empirically show the relation between certain function landscape properties and M-CMA-ES performance