1,724 research outputs found
a cross-entropy based multiagent approach for multiclass activity chain modeling and simulation
This paper attempts to model complex destination-chain, departure time and route choices based on activity plan implementation and proposes an arc-based cross entropy method for solving approximately the dynamic user equilibrium in multiagent-based multiclass network context. A multiagent-based dynamic activity chain model is developed, combining travelers' day-to-day learning process in the presence of both traffic flow and activity supply dynamics. The learning process towards user equilibrium in multiagent systems is based on the framework of Bellman's principle of optimality, and iteratively solved by the cross entropy method. A numerical example is implemented to illustrate the performance of the proposed method on a multiclass queuing network.dynamic traffic assignment, cross entropy method, activity chain, multiagent, Bellman equation
Multiagent Deep Reinforcement Learning: Challenges and Directions Towards Human-Like Approaches
This paper surveys the field of multiagent deep reinforcement learning. The
combination of deep neural networks with reinforcement learning has gained
increased traction in recent years and is slowly shifting the focus from
single-agent to multiagent environments. Dealing with multiple agents is
inherently more complex as (a) the future rewards depend on the joint actions
of multiple players and (b) the computational complexity of functions
increases. We present the most common multiagent problem representations and
their main challenges, and identify five research areas that address one or
more of these challenges: centralised training and decentralised execution,
opponent modelling, communication, efficient coordination, and reward shaping.
We find that many computational studies rely on unrealistic assumptions or are
not generalisable to other settings; they struggle to overcome the curse of
dimensionality or nonstationarity. Approaches from psychology and sociology
capture promising relevant behaviours such as communication and coordination.
We suggest that, for multiagent reinforcement learning to be successful, future
research addresses these challenges with an interdisciplinary approach to open
up new possibilities for more human-oriented solutions in multiagent
reinforcement learning.Comment: 37 pages, 6 figure
Approaches for Future Internet architecture design and Quality of Experience (QoE) Control
Researching a Future Internet capable of overcoming the current Internet limitations is a strategic
investment. In this respect, this paper presents some concepts that can contribute to provide some guidelines to
overcome the above-mentioned limitations. In the authors' vision, a key Future Internet target is to allow
applications to transparently, efficiently and flexibly exploit the available network resources with the aim to
match the users' expectations. Such expectations could be expressed in terms of a properly defined Quality of
Experience (QoE). In this respect, this paper provides some approaches for coping with the QoE provision
problem
Learning Complex Teamwork Tasks Using a Given Sub-task Decomposition
Training a team to complete a complex task via multi-agent reinforcement
learning can be difficult due to challenges such as policy search in a large
joint policy space, and non-stationarity caused by mutually adapting agents. To
facilitate efficient learning of complex multi-agent tasks, we propose an
approach which uses an expert-provided decomposition of a task into simpler
multi-agent sub-tasks. In each sub-task, a subset of the entire team is trained
to acquire sub-task-specific policies. The sub-teams are then merged and
transferred to the target task, where their policies are collectively
fine-tuned to solve the more complex target task. We show empirically that such
approaches can greatly reduce the number of timesteps required to solve a
complex target task relative to training from-scratch. However, we also
identify and investigate two problems with naive implementations of approaches
based on sub-task decomposition, and propose a simple and scalable method to
address these problems which augments existing actor-critic algorithms. We
demonstrate the empirical benefits of our proposed method, enabling sub-task
decomposition approaches to be deployed in diverse multi-agent tasks
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