Multi-agent discrete-time graphical games and reinforcement learning solutions
نویسندگان
چکیده
منابع مشابه
Multi-agent discrete-time graphical games and reinforcement learning solutions
This paper introduces a new class of multi-agent discrete-time dynamic games, known in the literature as dynamic graphical games. For that reason a local performance index is defined for each agent that depends only on the local information available to each agent. Nash equilibrium policies and best-response policies are given in terms of the solutions to the discrete-time coupled Hamilton–Jaco...
متن کاملReinforcement Learning in Multi-agent Games
This article investigates the performance of independent reinforcement learners in multiagent games. Convergence to Nash equilibria and parameter settings for desired learning behavior are discussed for Q-learning, Frequency Maximum Q value (FMQ) learning and lenient Q-learning. FMQ and lenient Q-learning are shown to outperform regular Q-learning significantly in the context of coordination ga...
متن کاملOnline Adaptive Learning Solution of Multi-Agent Differential Graphical Games
Distributed networks have received much attention in the last year because of their flexibility and computational performance. The ability to coordinate agents is important in many real-world tasks where it is necessary for agents to exchange information with each other. Synchronization behavior among agents is found in flocking of birds, schooling of fish, and other natural systems. Work has b...
متن کاملMarkov Games of Incomplete Information for Multi-Agent Reinforcement Learning
Partially observable stochastic games (POSGs) are an attractive model for many multi-agent domains, but are computationally extremely difficult to solve. We present a new model, Markov games of incomplete information (MGII) which imposes a mild restriction on POSGs while overcoming their primary computational bottleneck. Finally we show how to convert a MGII into a continuous but bounded fully ...
متن کاملMarkov Games as a Framework for Multi-Agent Reinforcement Learning
In the Markov decision process (MDP) formalization of reinforcement learning, a single adaptive agent interacts with an environment defined by a probabilistic transition function. In this solipsistic view, secondary agents can only be part of the environment and are therefore fixed in their behavior. The framework of Markov games allows us to widen this view to include multiple adaptive agents ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Automatica
سال: 2014
ISSN: 0005-1098
DOI: 10.1016/j.automatica.2014.10.047