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Category Archives: Reinforcement Learning
How to carry out an operator with probability p – Optimization Algorithms
When implementing optimization algorithms, we often bump into this phrase: “an operator needs to be carried out with probability P”. For instance, this can happen when implementing the crossover or mutation phases in Evolutionary Algorithms. It means that you want … Continue reading
Posted in Linux, Machine Learning, MATLAB, Optimization, programming, Reinforcement Learning, Robotics, Software, Statistics, Ubuntu
Tagged algorithm, code, distribution, easy, example, linux, Machine Learning, math, mathematics, Matlab, operator, optimization, probability, programming, random, robot, sampling, statistics, step, step by step, tutorial, uniform, variable, windows
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CMA-ES
The CMA Evolution Strategy The CMA-ES (Covariance Matrix Adaptation Evolution Strategy) is an evolutionary algorithm for difficult non-linear non-convex optimization problems in continuous domain. The CMA-ES is typically applied to unconstrained or bounded constraint optimization problems, and search space dimensions between three and a hundred. The … Continue reading
Use Simulated Annealing in command-line (MATLAB)
As you can see the first step is to set the options using the function saoptimset. And then use the main function easily. Notice that the Simulated Annealing can be used from Optimization Toolbox of the MATLAB directly.
Posted in Linux, Machine Learning, MATLAB, Optimization, programming, Reinforcement Learning, Robotics, Software, Ubuntu
Tagged annealing, command line, global, linux, Machine Learning, Matlab, minimization, optimization, option, option setting, parameters, programming, Reinforcement Learning, script, set, simulated, windows
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What is Heuristic?
Definition (Heuristic). A heuristic is a part of an optimization algorithm that uses the information currently gathered by the algorithm to help to decide which solution candidate should be tested next or how the next individual can be produced. Heuristics are usually problem class … Continue reading
Posted in Machine Learning, Optimization, Reinforcement Learning, Statistics, Thoughts
Tagged deterministic, global, local, Machine Learning, optimization, probabilistic
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Three famous metrics (Manhattan, Euclidean, Minkowski)
Three famous metrics (to calculate the distance between two points): 1- Manhattan distance 2- Euclidean distance 3-Minkowski distance Equations: N-dimensional Manhattan distance 3 dimensional Euclidean distance N dimensional Euclidean distance N-dimensional Minkowski distance … Continue reading
Posted in Machine Learning, MATLAB, Optimization, programming, Reinforcement Learning, Robotics, Statistics
Tagged 3, dimension, distance, equation, Euclidean, Machine Learning, Manhattan, mathematics, metric, Minkowski, N, point, space, statistics
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How to calculate Mean value/ Expected Value (1)
There are 3 main kind of MEAN: Arithmetic Mean / Geometric Mean / Harmonic Mean you can find related formulation here. And here is the MATLAB code to calculate 3 different types of Mean for a sample vector: %% How … Continue reading
Posted in MATLAB, programming, Reinforcement Learning, Statistics
Tagged code, example, expected value, Machine Learning, Matlab, mean, statistics
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How to send/receive data in a 2-way UDP connection, using MATLAB
1- you have to define the ip and port for both PCs 2- open the session 3- make some loops to receive, calculate and send data inside: 4- Finally you have to close port and delete the obj from … Continue reading
Posted in Linux, MATLAB, programming, Reinforcement Learning, Robotics, Software, Ubuntu, Uncategorized
Tagged code, connection, data, easy, example, ip, linux, Matlab, port, programming, receive, send, session, software, source, step by step, tutorial, udp, windows
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Rewards VS. Values (in the concept of Reinforcement Learning)
In the concept of Reinforcement Learning, we usually deal with two pretty similar keywords, reward and value, that can be confusing for beginners. In this brief post, I will mention the differences and actual definitions of these terms. Rewards are … Continue reading
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