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The Teachingbox uses advanced machine learning techniques to relieve developers from the programming of hand-crafted sophisticated behaviors of autonomous agents (such as robots, game players etc...) In the current status we have implemented a well founded reinforcement learning core in Java with many popular usecases, environments, policies and learners.
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A platform for rapid Reinforcement Learning methods development
Application allowing convenient experimentation in Reinforcement Learning - a Machine Learning domain. Project goals are:
- keep adding new environments and agents as simple as possible
- provide a rich set of state-of-art algorithms and problems
- integrate with other existing Reinforcement Learning platforms
If you found this application useful please cite this work: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6643987
Highly modularized Reinforcement Learning library for real/simulation robots to learn behaviors. Our ultimate goal is to develop an artificial intelligence (AI) program with which the robots can learn to behave as their users wish.
This project provides a framework for testing and comparing different machine learning algorithms (particularly reinforcement learning methods) in different scenarios. Its intended area of application is in research and education.
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This is a third year computer science project.
A software system for simulating and animating Reinforcement Learning (RL) algorithms mainly for modular robots.
A Python class library of tools for learning agents, including reinforcement learning algorithms, function approximators, and vector quantizations algorithms. (Pronounced "plastic".)
AppSignal's MCP server hands Claude, Cursor, or Zed your real errors, traces, and the deploy that shipped them. AI writes the fix; you review the diff.
RL Poker is a study project Java implementation of an e-soft on-policy Monte Carlo Texas Hold'em poker reinforcement learning algoritm with a feedforward neural network and backpropagation. It provides a graphical interface to monitor game rounds.
General purpose agents using reinforcement learning. Combines radial basis functions, temporal difference learning, planning, uncertainty estimations, and curiosity. Intended to be an out-of-the-box solution for roboticists and game developers.