Algorithm Kit

Algorithm Kit is a comprehensive educational resource providing clean, well-documented implementations of fundamental algorithms in control theory, machine learning, and optimization. Designed for academic instruction and research, this collection offers production-ready Python implementations with detailed explanations, complexity analysis, and practical examples.

Each algorithm is carefully implemented with modern software engineering practices, comprehensive testing, and extensive documentation to serve as both a learning tool and a reliable reference for students and researchers.

Documentation · Source Code · GitHub

Algorithm Families

Explore the comprehensive collection of algorithm implementations organized by domain and application area.

Family	Algorithms	Completion	Status
Control Algorithms	Coming Soon	0%	Coming Soon
Dynamic Movement Primitives	Coming Soon	0%	Coming Soon
Dynamic Programming	Coin Change Problem ✓, 0/1 Knapsack Problem ✓, Fibonacci Sequence ✓, Edit Distance (Levenshtein Distance) ✓, Matrix Chain Multiplication ✓, Longest Common Subsequence (LCS) ✓	100%	Code
Gaussian Process	Coming Soon	0%	Coming Soon
Hierarchical Reinforcement Learning	Coming Soon	0%	Coming Soon
Model Predictive Control	Coming Soon	0%	Coming Soon
Planning Algorithms	M ✓, A Search ✓, Depth-First Search ✓, Breadth-First Search** ✓	100%	Code
Real-time Control	Coming Soon	0%	Coming Soon
Reinforcement Learning	Actor-Critic ✓, Deep Q-Network (DQN) ✓, Proximal Policy Optimization (PPO) ✓, Q-Learning ✓, SARSA (State-Action-Reward-State-Action) ✓, Policy Gradient ✓	100%	Code

Status legend: Code available · Coming Soon

Family Overviews

Each algorithm family represents a distinct computational paradigm with specific theoretical foundations and practical applications. Click through the families below to explore the algorithms, their mathematical foundations, and implementation details.

Control Algorithms Dynamic Movement Primitives Dynamic Programming Gaussian Process Hierarchical Reinforcement Learning Model Predictive Control Planning Algorithms Real-time Control Reinforcement Learning

Control Algorithms

Control algorithms provide methods to regulate system behavior, maintain desired outputs, and ensure stability under various operating conditions.

Completion: 0% (0 of 0 algorithms complete)

Coming soon - algorithms in development

Dynamic Movement Primitives

Dynamic Movement Primitives provide a framework for learning, representing, and reproducing complex motor behaviors in robotics and control systems.

Completion: 0% (0 of 0 algorithms complete)

Coming soon - algorithms in development

Dynamic Programming

Dynamic Programming solves complex problems by breaking them into overlapping subproblems with optimal substructure.

Completion: 100% (6 of 6 algorithms complete)

Algorithms:

• Coin Change Problem ✓ - Find the minimum number of coins needed to make a given amount using dynamic programming.
• 0/1 Knapsack Problem ✓ - Optimize value within weight constraints using dynamic programming for resource allocation.
• Fibonacci Sequence ✓ - Classic sequence where each number is the sum of the two preceding ones, demonstrating recursion, memoization, and dynamic programming.
• Edit Distance (Levenshtein Distance) ✓ - Calculate minimum operations to transform one string into another using dynamic programming.
• Matrix Chain Multiplication ✓ - Find optimal parenthesization for matrix chain multiplication to minimize operations using dynamic programming.
• Longest Common Subsequence (LCS) ✓ - Find the longest subsequence common to two sequences using dynamic programming.

Gaussian Process

Gaussian Process algorithms provide probabilistic machine learning methods for regression, classification, and optimization with uncertainty quantification.

Completion: 0% (0 of 0 algorithms complete)

Coming soon - algorithms in development

Hierarchical Reinforcement Learning

Hierarchical Reinforcement Learning decomposes complex tasks into simpler subtasks using temporal abstraction and multi-level decision making.

Completion: 0% (0 of 0 algorithms complete)

Coming soon - algorithms in development

Model Predictive Control

Model Predictive Control optimizes control actions by solving constrained optimization problems over a prediction horizon.

Completion: 0% (0 of 0 algorithms complete)

Coming soon - algorithms in development

Planning Algorithms

Planning algorithms solve sequential decision problems by finding optimal sequences of actions to achieve goals from initial states.

Completion: 100% (4 of 4 algorithms complete)

Algorithms:

• M* ✓ - Multi-agent pathfinding algorithm that finds collision-free paths for multiple agents by resolving conflicts and coordinating their movements.
• A* Search ✓ - Optimal pathfinding algorithm that uses heuristics to efficiently find the shortest path from start to goal in weighted graphs.
• Depth-First Search ✓ - Graph traversal algorithm that explores as far as possible along each branch before backtracking, using stack-based or recursive implementation.
• Breadth-First Search ✓ - Graph traversal algorithm that explores all nodes at the current depth level before moving to the next level, guaranteeing shortest path in unweighted graphs.

Real-time Control

Real-time control algorithms provide deterministic, time-critical control solutions for systems requiring guaranteed response times and predictable behavior.

Completion: 0% (0 of 0 algorithms complete)

Coming soon - algorithms in development

Reinforcement Learning

Reinforcement Learning enables agents to learn optimal behavior through interaction with an environment using rewards and penalties.

Completion: 100% (6 of 6 algorithms complete)

Algorithms:

• Actor-Critic ✓ - A hybrid reinforcement learning algorithm that combines policy gradient methods with value function approximation for improved learning efficiency.
• Deep Q-Network (DQN) ✓ - A deep reinforcement learning algorithm that uses neural networks to approximate Q-functions for high-dimensional state spaces.
• Proximal Policy Optimization (PPO) ✓ - A state-of-the-art policy gradient algorithm that uses clipped objective to ensure stable policy updates with improved sample efficiency.
• Q-Learning ✓ - A model-free reinforcement learning algorithm that learns optimal action-value functions through temporal difference learning.
• SARSA (State-Action-Reward-State-Action) ✓ - A model-free, on-policy reinforcement learning algorithm that learns action-value functions while following the current policy.
• Policy Gradient ✓ - A policy-based reinforcement learning algorithm that directly optimizes the policy using gradient ascent on expected returns.

Getting Started

Installation and Setup

To begin using Algorithm Kit in your research or coursework:

• Install: uv pip install -e . (editable development install)
• Verify: just test (run the comprehensive test suite)
• Quality Check: just lint (ensure code quality standards)

Command LinePython

# Install Algorithm Kit in development mode
uv pip install -e .

# Verify all implementations with comprehensive tests
just test

# Ensure code quality and style compliance
just lint

# Import the package
import algokit

# Your algorithm implementations here
print("Algorithm Kit is ready!")

Academic Use

This resource is designed for educational and research purposes. Each algorithm includes theoretical background, complexity analysis, and practical implementation details suitable for coursework and research projects.

Key Features

• Academic Quality: Rigorous implementations with theoretical foundations and complexity analysis
• Educational Focus: Comprehensive documentation designed for learning and teaching
• Research Ready: Production-quality code suitable for academic research and publication
• Comprehensive Testing: Extensive test suites ensuring correctness and reliability
• Type Safety: Full type annotations for better code understanding and IDE support
• Modular Design: Clean architecture enabling easy extension and customization

Implementation Standards

• Modern Python: Built with Python 3.12+ and contemporary best practices
• Rigorous Testing: pytest with comprehensive coverage ensuring algorithm correctness
• Code Quality: Automated linting and formatting for consistent, readable implementations
• Type Safety: Complete type annotations for better code understanding and IDE support
• Documentation: Professional documentation with mathematical formulations and examples
• Version Control: Git-based workflow with automated quality assurance

Development

For more detailed information about the project architecture, development guidelines, and quality standards, please refer to the project documentation in the main repository.

API Reference

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