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Open-Source Activity

Implementation of Reinforcement Learning Algorithms

Concierge, a travel agency startup, hired me to design their Berlin guide. It was a very small map that was meant to fold and fit in your back pocket.

Implementations of major reinforcement learning (RL) algorithms using Python and PyTorch. Each of them links to the respective code on GitHub:

Monte-Carlo Methods
Temporal Difference Algorithms
Eligibility Traces
Function Approximation
- Q-Learning with Linear Function Approximator
- SARSA with Linear Function Approximator
Policy Gradients
- REINFORCE with Baseline
- Actor-Crtic Algorithm on CliffWalking Environment
RL Algorithms using PyTorch

All Implementation of Machine Learning Algorithms Image Colorizer using Neural Networks Probablistic Search and Destroy Minesweeper AI Bot Mazerunner - Analysing AI Search Algorithms Music Genre Belief Recognition using Neural Networks Statistics - 101 Optimal Stock Portfolio Management using Deep Reinforcement Learning Predict Stock Returns using GloVe Embeddings and Document Vectors Kaggle Competitions Implementation of Reinforcement Learning Algorithms Extraction of Professional Details from Web-URLs using DeepDive Table Header Detection and Classification using Machine Learning

Implementation of Machine Learning Algorithms Image Colorizer using Neural Networks Probablistic Search and Destroy Minesweeper AI Bot Mazerunner - Analysing AI Search Algorithms Music Genre Belief Recognition using Neural Networks Statistics - 101 Optimal Stock Portfolio Management using Deep Reinforcement Learning Predict Stock Returns using GloVe Embeddings and Document Vectors Kaggle Competitions Implementation of Reinforcement Learning Algorithms Extraction of Professional Details from Web-URLs using DeepDive Table Header Detection and Classification using Machine Learning