Evolutionary algorithms and reinforcement learning in experiments with slot cars

2013 ◽  
Author(s):  
Dan Martinec ◽  
Marek Bundzel
Keyword(s):  
Reinforcement Learning ◽  
Evolutionary Algorithms

scholarly journals BOLeRo: Behavior optimization and learning for robots

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142091374
Author(s):  
Alexander Fabisch ◽  
Malte Langosz ◽  
Frank Kirchner
Keyword(s):  
Reinforcement Learning ◽  
Evolutionary Algorithms ◽  
Open Source ◽  
State Of The Art ◽  
Learning Algorithms ◽  
Robotic Systems ◽  
Policy Search ◽  
Behavior Learning ◽  
And Behavior

Reinforcement learning and behavior optimization are becoming more and more popular in the field of robotics because algorithms are mature enough to tackle real problems in this domain. Robust implementations of state-of-the-art algorithms are often not publicly available though, and experiments are hardly reproducible because open-source implementations are often not available or are still in a stage of research code. Consequently, often it is infeasible to deploy these algorithms on robotic systems. BOLeRo closes this gap for policy search and evolutionary algorithms by delivering open-source implementations of behavior learning algorithms for robots. It is easy to integrate in robotic middlewares and it can be used to compare methods and develop prototypes in simulation.

scholarly journals Evolutionary Algorithms for Reinforcement Learning

1999 ◽  
Vol 11 ◽  
pp. 241-276 ◽  
Author(s):  
D. E. Moriarty ◽  
A. C. Schultz ◽  
J. J. Grefenstette
Keyword(s):  
Reinforcement Learning ◽  
Evolutionary Algorithms ◽  
Value Function ◽  
Learning Problems ◽  
Temporal Difference ◽  
Genetic Operators ◽  
Credit Assignment ◽  
Learning Problem ◽  
Difference Methods ◽  
Temporal Difference Methods

There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal difference methods and evolutionary algorithms are well-known examples of these approaches. Kaelbling, Littman and Moore recently provided an informative survey of temporal difference methods. This article focuses on the application of evolutionary algorithms to the reinforcement learning problem, emphasizing alternative policy representations, credit assignment methods, and problem-specific genetic operators. Strengths and weaknesses of the evolutionary approach to reinforcement learning are presented, along with a survey of representative applications.

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