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Monthly Archives: March 2012
Solving a Boundary Value Problem in MATLAB – An Example
In this post, I demonstrate a simple function to solve a BVP problem. Here the problem is an ODE. After solving the problem, we plot the result. Copy- paste , all the below code into an m-file in MATLAB and … Continue reading
Posted in Linux, Machine Learning, MATLAB, programming, Software, Ubuntu
Tagged boundary, boundary condition, boundary value problem, bvp, code, example, linux, Matlab, ode, ordinary differential equation, programming, sample, simple, software, solve, 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
Elements
elements of Unsupervised Learning (Reinforcement Learning) 1-Agent (behaviour at a given time) 2-Environment 3-Policy 4-Value Function (specifies what is good in the long run) 5-Reward Function (Define Goal – what is good in an immediate sense) 6-Model of the environment … Continue reading
General Categorization of Machine Learning methods
There are three main branches of learning methods: 1-Supervised learning – These methods require labeled data to learn from. For instance given a 10000 images of cats and dogs which are labeled correctly by a supervisor, a machine learning approach … Continue reading
Posted in Machine Learning, Neural Networks, Reinforcement Learning, Robotics, Thoughts
Tagged action, anomaly detection, bayes, bayes classifier, classification, classifier, clustering, data, direct policy search, fuzzy, gaussian, gaussian process, k-means, label, labeled, Machine Learning, neural networks, observation, q-learning, radial basis network, Reinforcement Learning, reward, sarsa, state, supervised, supervised learning, td-learning, unsupervised, unsupervised learning
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Policy Function – definition
(An Introduction to Reinforcement Learning) – Part.1 Policy Function: defines action related to a state / maps any state to a related action. There are three kinds of Policies: 1- Stochastic Policy: (probability – uses a transition function that … Continue reading
Posted in Machine Learning, Reinforcement Learning, Robotics
Tagged Machine Learning, Policy, Reinforcement Learning, Robotics
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machinelearning1 