Multi-Armed Bandit (MAB) algorithm implementation in go
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Updated
Nov 25, 2019 - Go
Multi-Armed Bandit (MAB) algorithm implementation in go
Implementations of basic concepts dealt under the Reinforcement Learning umbrella. This project is collection of assignments in CS747: Foundations of Intelligent and Learning Agents (Autumn 2017) at IIT Bombay
Autonomous Agent Multi-Armed Bandit UCB1 & Thompson Sampling Exploration-Exploitation Optimizer
Autonomous Agent Multi-Armed Bandit UCB1 & Thompson Sampling Exploration-Exploitation Optimizer
Multi-armed bandit decision engine with Upper Confidence Bound 1 (UCB1) and Bayesian Thompson Sampling.
Multi-armed bandit decision engine with Upper Confidence Bound 1 (UCB1) and Bayesian Thompson Sampling.
Few-shot prompting using Contextual Combinatorial Bandit optimizations
This project focuses on comparing different Reinforcement Learning Algorithms, including monte-carlo, q-learning, lambda q-learning epsilon-greedy variations, etc.
A Contextual-bandit approach on MIND Datasets for News Recommendation Systems.
Explore key RL algorithms with detailed explanations and fully commented Python code implementations
An implementation of solvers for the multi-armed-bandit-problem in JavaScript.
Time-bounded Monte Carlo Tree Search agent and tournament framework for Connect Four
Reversi (Othello) AI game in C#. Using Monte Carlo Tree Search algorithm AND BTMM algorithm.
This repository is focused on my assignments solutions for the Statistical Techniques for Data Science course at Innopolis University.
New Z2I!!!
Retrosynthesis planning via Monte Carlo Tree Search (MCTS) with UCB1 — PyTorch template-scoring MLP + RDKit reaction SMARTS + purchasability oracle. Inspired by Gao, Mercado & Coley (ICLR 2022).
Reinforcement learning techniques applied to solve pricing problems in e-commerce applications. Final project for "Online learning applications" course (2021-2022)
Python implementation of Monte Carlo Tree Search
Let's play Checkers / Draughts here
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