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Tag Archives: Reinforcement Learning
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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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
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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