The Quantitative Law of Effect is a Robust Emergent Property of an Evolutionary Algorithm for Reinforcement Learning

2005 ◽  
pp. 413-422 ◽  
Author(s):  
J. J McDowell ◽  
Zahra Ansari
Keyword(s):  
Reinforcement Learning ◽  
Evolutionary Algorithm ◽  
Emergent Property

A Graph-Based Evolutionary Algorithm: Genetic Network Programming (GNP) and Its Extension Using Reinforcement Learning

2007 ◽  
Vol 15 (3) ◽  
pp. 369-398 ◽  
Author(s):  
Shingo Mabu ◽  
Kotaro Hirasawa ◽  
Jinglu Hu
Keyword(s):  
Reinforcement Learning ◽  
Evolutionary Algorithm ◽  
Past History ◽  
Structural Characteristics ◽  
Memory Function ◽  
Genetic Network ◽  
Dynamic Environments ◽  
Network Programming ◽  
History Of ◽  
Genetic Network Programming

This paper proposes a graph-based evolutionary algorithm called Genetic Network Programming (GNP). Our goal is to develop GNP, which can deal with dynamic environments efficiently and effectively, based on the distinguished expression ability of the graph (network) structure. The characteristics of GNP are as follows. 1) GNP programs are composed of a number of nodes which execute simple judgment/processing, and these nodes are connected by directed links to each other. 2) The graph structure enables GNP to re-use nodes, thus the structure can be very compact. 3) The node transition of GNP is executed according to its node connections without any terminal nodes, thus the past history of the node transition affects the current node to be used and this characteristic works as an implicit memory function. These structural characteristics are useful for dealing with dynamic environments. Furthermore, we propose an extended algorithm, “GNP with Reinforcement Learning (GNPRL)” which combines evolution and reinforcement learning in order to create effective graph structures and obtain better results in dynamic environments. In this paper, we applied GNP to the problem of determining agents' behavior to evaluate its effectiveness. Tileworld was used as the simulation environment. The results show some advantages for GNP over conventional methods.

scholarly journals An oppositional-cauchy based GSK evolutionary algorithm with a novel deep ensemble reinforcement learning strategy for COVID-19 diagnosis

2021 ◽  
pp. 107675
Author(s):  
Seyed Mohammad Jafar Jalali ◽  
Milad Ahmadian ◽  
Sajad Ahmadian ◽  
Abbas Khosravi ◽  
Mamoun Alazab ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Evolutionary Algorithm ◽  
Learning Strategy

scholarly journals Genetic Network Programming with Actor-Critic

2007 ◽  
Vol 11 (1) ◽  
pp. 79-86
Author(s):  
Hiroyuki Hatakeyama ◽  
◽  
Shingo Mabu ◽  
Kotaro Hirasawa ◽  
Jinglu Hu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Evolutionary Algorithm ◽  
Genetic Network ◽  
Network Programming ◽  
Task Execution ◽  
Discrete Information ◽  
New Type ◽  
As Graph ◽  
And Performance ◽  
Genetic Network Programming

A new graph-based evolutionary algorithm named “Genetic Network Programming, GNP” has been already proposed. GNP represents its solutions as graph structures, which can improve the expression ability and performance. In addition, GNP with Reinforcement Learning (GNP-RL) was proposed a few years ago. Since GNP-RL can do reinforcement learning during task execution in addition to evolution after task execution, it can search for solutions efficiently. In this paper, GNP with Actor-Critic (GNP-AC) which is a new type of GNP-RL is proposed. Originally, GNP deals with discrete information, but GNP-AC aims to deal with continuous information. The proposed method is applied to the controller of the Khepera simulator and its performance is evaluated.

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