No abstract

Surfing on the internet boom, the digital marketing industry has seen an exponential growth in the recent years and is often at the origin of the financial success of the biggest tech firms. In this paper we study the current landscape of this industry and comment on the monopoly that Google has managed to gain over the years through technical innovations and intelligent acquisitions. We then propose potential avenues to explore in an effort to help moving the digital marketing industry towards a fairer model.

Agent based modeling (ABM) is a computational approach to modeling complex systems by specifying the behavior of autonomous decision-making components or agents in the system and allowing the system dynamics to emerge from their interactions. Recent advances in the field of Multi-agent reinforcement learning (MARL) have made it feasible to learn the equilibrium of complex environments where multiple agents learn at the same time -opening up the possibility of building ABMs where agent behaviors are learned and system dynamics can be analyzed. However, most ABM frameworks are not RL-native, in that they do not offer concepts and interfaces that are compatible with the use of MARL to learn agent behaviors. In this paper, we introduce a new framework, Phantom, to bridge the gap between ABM and MARL. Phantom is an RL-driven framework for agent-based modeling of complex multiagent systems such as economic systems and markets. To enable this, the framework provides tools to specify the ABM in MARLcompatible terms -including features to encode dynamic partial observability, agent utility / reward functions, heterogeneity in agent preferences or types, and constraints on the order in which agents can act (e.g. Stackelberg games, or complex turn-taking environments). In this paper, we present these features, their design rationale and show how they were used to model and simulate Over-The-Counter (OTC) markets. CCS CONCEPTS• Software and its engineering → Application specific development environments; • Theory of computation → Multiagent reinforcement learning; Market equilibria.

No abstract

In this paper, we evaluate the use of Reinforcement Learning (RL) to solve a classic combinatorial optimization problem: the Capacitated Vehicle Routing Problem (CVRP). We formalize this problem in the RL framework and compare two of the most promising RL approaches with traditional solving techniques on a set of benchmark instances. We measure the different approaches with the quality of the solution returned and the time required to return it. We found that despite not returning the best solution, the RL approach has many advantages over traditional solvers. First, the versatility of the framework allows the resolution of more complex combinatorial problems. Moreover, instead of trying to solve a specific instance of the problem, the RL algorithm learns the skills required to solve the problem. The trained policy can then quasi instantly provide a solution to an unseen problem without having to solve it from scratch. Finally, the use of trained models makes the RL solver by far the fastest, and therefore make this approach more suited for commercial use where the user experience is paramount. Techniques like Knowledge Transfer can also be used to improve the training efficiency of the algorithm and help solve bigger and more complex problems.