Imitation Learning of Team-play in Multiagent System based on Hidden Markov Modeling
نویسنده
چکیده
This paper addresses agents' intentions as building blocks of imitation learning that abstract local situations of the agent, and proposes a hierarchical hidden Markov model (HMM) to represent cooperative behaviors of teamworks. The key of the proposed model is introduction of gate probabilities that restrict transition among agents' intentions according to others' intentions. Using these probabilities, the framework can control transitions flexibly among basic behaviors in a cooperative behavior.
منابع مشابه
Reinforcement Learning to Play an Optimal Nash Equilibrium in Team Markov Games
Multiagent learning is a key problem in AI. In the presence of multiple Nash equilibria, even agents with non-conflicting interests may not be able to learn an optimal coordination policy. The problem is exaccerbated if the agents do not know the game and independently receive noisy payoffs. So, multiagent reinforfcement learning involves two interrelated problems: identifying the game and lear...
متن کاملLearning Trust in Dynamic Multiagent Environments using HMMs
In open multiagent systems, agents are owned by a variety of stakeholders and can enter and leave the system at any time. Therefore, trust is a fundamental concern in effective interactions which is a key component of such systems. In this paper, we propose a trust model for autonomous agents in mulitagent environments based on hidden Markov models and reinforcement learning. By this combinatio...
متن کاملIntrusion Detection Using Evolutionary Hidden Markov Model
Intrusion detection systems are responsible for diagnosing and detecting any unauthorized use of the system, exploitation or destruction, which is able to prevent cyber-attacks using the network package analysis. one of the major challenges in the use of these tools is lack of educational patterns of attacks on the part of the engine analysis; engine failure that caused the complete training, ...
متن کاملA Multiagent Reinforcement Learning algorithm to solve the Community Detection Problem
Community detection is a challenging optimization problem that consists of searching for communities that belong to a network under the assumption that the nodes of the same community share properties that enable the detection of new characteristics or functional relationships in the network. Although there are many algorithms developed for community detection, most of them are unsuitable when ...
متن کاملVision-Based Imitation Learning in Heterogeneous Multi-Robot Systems: Varying Physiology and Skill
Imitation learning enables a learner to improve its abilities by observing others. Most robotic imitation learning systems only learn from demonstrators that are similar physically and in terms of skill level. In order to employ imitation learning in a heterogeneous multi-agent environment, we must consider both differences in skill, and physical differences (physiology, size). This paper descr...
متن کامل