Abmarl: Connecting Agent-Based Simulations with Multi-Agent Reinforcement Learning

This chapter demonstrates an application of agent-based selection dynamics to the traffic assignment problem. We introduce an evolutionary dynamic approach that acquires payoff data from multi-agent reinforcement learning to enable an adaptive optimization of traffic assignment, provided that classical theories of traffic user equilibrium pose the problem as one of global optimization. We then show how these data can be employed to define the conditions for evolutionary stability and Nash equilibria. The validity of this method is demonstrated by studies in traffic network

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An Adaptive Market-driven Agent Based on Multi-agent Reinforcement Learning for Automated Negotiation

International Journal of Digital Content Technology and its Applications ◽

10.4156/jdcta.vol6.issue2.6 ◽

2012 ◽

Vol 6 (2) ◽

pp. 43-51 ◽

Cited By ~ 1

Author(s):

Chun Shen ◽

Lei Liu ◽

Fan Luo ◽

Yi Lu ◽

Shuai Lu

Keyword(s):

Reinforcement Learning ◽

Automated Negotiation ◽

Agent Based ◽

Multi Agent ◽

Market Driven

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REINFORCEMENT LEARNING IN SUPPLY CHAINS

International Journal of Neural Systems ◽

10.1142/s0129065709002063 ◽

2009 ◽

Vol 19 (05) ◽

pp. 331-344 ◽

Cited By ~ 15

Author(s):

ANNAPURNA VALLURI ◽

MICHAEL J. NORTH ◽

CHARLES M. MACAL

Keyword(s):

Artificial Intelligence ◽

Cognitive Psychology ◽

Reinforcement Learning ◽

Supply Chains ◽

Human Beings ◽

Agent Based ◽

Multi Stage ◽

Long Time ◽

Multi Agent ◽

First Time

Effective management of supply chains creates value and can strategically position companies. In practice, human beings have been found to be both surprisingly successful and disappointingly inept at managing supply chains. The related fields of cognitive psychology and artificial intelligence have postulated a variety of potential mechanisms to explain this behavior. One of the leading candidates is reinforcement learning. This paper applies agent-based modeling to investigate the comparative behavioral consequences of three simple reinforcement learning algorithms in a multi-stage supply chain. For the first time, our findings show that the specific algorithm that is employed can have dramatic effects on the results obtained. Reinforcement learning is found to be valuable in multi-stage supply chains with several learning agents, as independent agents can learn to coordinate their behavior. However, learning in multi-stage supply chains using these postulated approaches from cognitive psychology and artificial intelligence take extremely long time periods to achieve stability which raises questions about their ability to explain behavior in real supply chains. The fact that it takes thousands of periods for agents to learn in this simple multi-agent setting provides new evidence that real world decision makers are unlikely to be using strict reinforcement learning in practice.

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