Multi-Agent Reinforcement Learning Model based on Fuzzy Inference
نویسندگان
چکیده
منابع مشابه
Fuzzy Model-Based Reinforcement Learning
Model-based reinforcement learning methods are known to be highly efficient with respect to the number of trials required for learning optimal policies. In this article, a novel fuzzy model-based reinforcement learning approach, fuzzy prioritized sweeping (F-PS), is presented. The approach is capable of learning strategies for Markov decision problems with continuous state and action spaces. Th...
متن کاملMulti-Agent Reinforcement Learning
This thesis presents a novel approach to provide adaptive mechanisms to detect and categorise Flooding-Base DoS (FBDoS) and Flooding-Base DDoS (FBDDoS) attacks. These attacks are generally based on a flood of packets with the intention of overfilling key resources of the target, and today the attacks have the capability to disrupt networks of almost any size. To address this problem we propose ...
متن کاملThree Perspectives on Multi-Agent Reinforcement Learning
This chapter concludes three perspectives on multi-agent reinforcement learning (MARL): (1) cooperative MARL, which performs mutual interaction between cooperative agents; (2) equilibrium-based MARL, which focuses on equilibrium solutions among gaming agents; and (3) best-response MARL, which suggests a no-regret policy against other competitive agents. Then the authors present a general framew...
متن کاملMulti-Agent Deep Reinforcement Learning
This work introduces a novel approach for solving reinforcement learning problems in multi-agent settings. We propose a state reformulation of multi-agent problems in R that allows the system state to be represented in an image-like fashion. We then apply deep reinforcement learning techniques with a convolution neural network as the Q-value function approximator to learn distributed multi-agen...
متن کاملMulti-agent Relational Reinforcement Learning
In this paper we study Relational Reinforcement Learning in a multi-agent setting. There is growing evidence in the Reinforcement Learning research community that a relational representation of the state space has many benefits over a propositional one. Complex tasks as planning or information retrieval on the web can be represented more naturally in relational form. Yet, this relational struct...
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: The Journal of the Korea Contents Association
سال: 2009
ISSN: 1598-4877
DOI: 10.5392/jkca.2009.9.10.051