Hamid R. Berenji

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A method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. It is shown that: the generalized approximate-reasoning-based intelligent control (GARIC) architecture learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; introduces a(More)
This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's(More)
| This paper presents the rst results in understanding the reasons for cooperative advantage between reinforcement learning agents. We consider a cooperation method which consists of using and updating a common policy. We tested this method on a complex fuzzy reinforcement learning problem and found that cooperation brings larger than expected bene ts. More(More)
This paper presents a new technique for the design of approximate reasoning-based controllers for dy­ namic physical systems with interacting goals. In this approach, goals are achieved based on a hier­ archy defined by a control knowledge base and re­ main highly interactive during the execution of the control task. The approach has been implemented in a(More)
Successful operations of future multi-agent intelligent systems require efficient cooperation schemes between agents sharing learning experiences. We consider a pseudo-reailstlc world in which one or more opportunities appear and disappear in random locations. Agents use fuzzy reinforcement learning to learn which opportunities are most worthy of pursuing(More)
Current reinforcement learning algorithms require long training periods which generally limit their applicability to small size problems. A new architecture is described which uses fuzzy rules to initialize its two neural networks: a neural network for performance evaluation and another for action selection. This architecture is applied to control of(More)
We address the issue of power-controlled shared channel access in wireless networks supporting packetized data traffic. We formulate this problem using the dynamic programming framework and present a new distributed fuzzy reinforcement learning algorithm (ACFRL-2) capable of adequately solving a class of problems to which the power control problem belongs.(More)