Search Results for "q learning algorithm" - Page 11
Sort By:
Showing 277 open source projects for "q learning algorithm"
View related business solutions-
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
1
Very basic cellular automaton implementation in C#. Based upon the "Togetherness" algorithm described at http://www.hermetic.ch/pca/tg.htm.
-
2
-
3
Linear Time Invariant (LTI) system identification using particle swarm optimization (PSO) algorithm. Creators : Vahid Kiani, Hadi Sadoghi Yazdi
-
4
TrimGA is a lightweight genetic algorithm library written in pure Java 6.0 that can be quickly applied to most optimization problems.
-
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
-
5
Java package to study a clustering model described in the paper \"Novel Clustering Algorithm Based Upon Games on Evolving Network\" by Q. Li, Z. Chen, Y. He and J-P. Jiang (in arxiv: http://arxiv.org/pdf/0812.5064v1), generalizations and similar issues.
-
6
iScore measures the interestingness of news articles in a limited user environment. It is an online learning algorithm that combines a large set of disparate features to classify documents. To download the source code, please use subversion.
-
7
TBLTools is a set of GATE processing resources that implements the Fast Transformation Based Learning Algorithm. You can train it to learn rules for NLP tasks such as Named Entity Recognition and Shallow parsing.
-
8
The Free Connectionist Q-learning Java Framework is an library for developing learning systems. Keywords: qlearning, artificial intelligence, alife, neural nets, neural networks, machine learning, reinforcement learning unsupervised learning agents lejos
-
9
PIQLE is a Platform Implementing Q-LEarning (and other Reinforcement Learning) algorithms in JAVA. Version 2 is a major refactoring. The core data structures and algorithms are in piqle-coreVersion2. Examples are in piqle-examplesVersion2. A complete doc
-
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
-
10
Classic game solved with MinMax algorithm (full solution tree) and simple Heuristics. Specially for learning and teaching computer AI
-
11
The Decision Tree Learning algorithm ID3 extended with pre-pruning for WEKA, the free open-source Java API for Machine Learning. It achieves better accuracy than WEKA's ID3, which lacks pre-pruning.Info: http://bruno-wp.blogspot.com/search/label/Softwar
-
12
A discretization algorithm based on the Minimum Description Length. Implemented as a filter according to the standards and interfaces of WEKA, the Java API for Machine Learning. More Info: http://bruno-wp.blogspot.com/search/label/Software
-
13
The project implements Dana Angluin's learning algorithm with intent to look into the possibility of computational learning of changing (shifting) information.
-
14
KNN-WEKA provides a implementation of the K-nearest neighbour algorithm for Weka. Weka is a collection of machine learning algorithms for data mining tasks. For more information on Weka, see http://www.cs.waikato.ac.nz/ml/weka/.
-
15
A Java implementation of the NEAT algorithm as created by Kenneth O Stanley. Also provides a toolkit for further experiments to be created and can provide both local and distributed learning environments.
-
16
In imitative learning, an agent will attempt to match what is observed to their memory. By visualizing the incorrectly matches "scenes", this project will allow algorithm developers to gain a better understanding of what causes their algorithms to fail.
-
17
MultiBoost is a C++ implementation of the multi-class AdaBoost algorithm. AdaBoost is a powerful meta-learning algorithm commonly used in machine learning. The code is well documented and easy to extend, especially for adding new weak learners.
-
18
Self study flashcards-based tool. Represents knowledge as question/answer pairs. Uses a scheduling algorithm for inter repetition invtervals optimization. Employs Web standards (XML, XSLT, HTML). Allows learning history interchange with similiar programs.
-
19
The TreeQ package is a set of C-language applications that implement a automatic machine learning algorithm based on a tree-structured classifier. This approach is particularly effective for high-dimensional continuous data such as audio and video.
-
20
The software is called 'Smart Stock Manager', and is a software tool developed the Artificial Neural Networks - Error Back Propagation algorithm. It basically takes input from the database, the past stock values and makes a study while learning by backpr
-
21
-
22
SimAlgPro - it is a tool to simulate the main algorithms for processor planning. to contribute to the learning process. It allows visualize of interactive form the algorithm application and results.
-
23
RL-POMDP is a Reinforcement Learning (RL) based algorithm to find approximate and satisfactory solution to POMDP problems. RL-POMDP is orders of magnitude faster than exact POMDP solver.
-
24
Java port and extension of MLC++ 2.0 by Kohavi et al. Currently contains ID3, C4.5, Naive (aka Simple) Bayes, and FSS and CHC (genetic algorithm) wrappers for feature selection. WEKA 3 interfaces are in development.
-
25
Algorithm that can generate any given series of probabilities G, using only fair coins. The algorithm creates a Huffman tree by decomposing any probability P into a sum of probabilites Q, where each Q is a power of 1/2. Than using the coins, the tra