Tree based discretization for continuous state space reinforcement learning
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
COOPERATIVE LEARNING BY POLICY-SHARING IN MULTIPLE AGENTS
Cybernetics and Systems
Reinforcement Learning: A Tutorial Survey and Recent Advances
INFORMS Journal on Computing
A novel approach for multi-agent-based Intelligent Manufacturing System
Information Sciences: an International Journal
A Dynamic Discretization Approach for Constructing Decision Trees with a Continuous Label
IEEE Transactions on Knowledge and Data Engineering
Ant colony optimization incorporated with fuzzy Q-learning for reinforcement fuzzy control
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A Q-learning approach to derive optimal consumption and investment strategies
IEEE Transactions on Neural Networks
A modified gradient-based neuro-fuzzy learning algorithm and its convergence
Information Sciences: an International Journal
Information Sciences: an International Journal
Optimal fuzzy control system using the cross-entropy method. A case study of a drilling process
Information Sciences: an International Journal
ANGLE: An autonomous, normative and guidable agent with changing knowledge
Information Sciences: an International Journal
Searching for overlapping coalitions in multiple virtual organizations
Information Sciences: an International Journal
Autonomic tracing of production processes with mobile and agent-based computing
Information Sciences: an International Journal
Self-organizing state aggregation for architecture design of Q-learning
Information Sciences: an International Journal
Information Sciences: an International Journal
Multiagent Reinforcement Learning: Spiking and Nonspiking Agents in the Iterated Prisoner's Dilemma
IEEE Transactions on Neural Networks
| Hi-index | 0.07 |
Reinforcement learning is one of the more prominent machine learning technologies, because of its unsupervised learning structure and its ability to produce continual learning, even in a dynamic operating environment. Applying this learning to cooperative multi-agent systems not only allows each individual agent to learn from its own experience, but also offers the opportunity for the individual agents to learn from other agents in the system, in order to increase the speed of learning. In the proposed learning algorithm, an agent stores its experience in terms of a state aggregation, by use of a decision tree, such that policy sharing between multiple agents is eventually accomplished by merging the different decision trees of peers. Unlike lookup tables, which have a homogeneous structure for state aggregation, decision trees carried with in agents have a heterogeneous structure. The method detailed in this study allows policy sharing between cooperative agents by means merging their trees into a hyper-structure, instead of forcefully merging entire trees. The proposed scheme initially allows the entire decision tree to be translated from one agent to others. Based on the evidence, only partial leaf nodes have useful experience for use in policy sharing. The proposed method induces a hyper decision tree by using a large amount of samples that are sampled from the shared nodes. The results from simulations in a multi-agent cooperative domain illustrate that the proposed algorithms perform better than the algorithm that does not allow sharing.