Version 1

Concepts

• Offline Reinforcement Learning
• Markov Decision Process

RL Tutorials

• Introduction to Gym toolkit
• Code-Driven Introduction to Reinforcement Learning
• CartPole using Cross-Entropy
• FrozenLake using Cross-Entropy
• FrozenLake using Value Iteration
• FrozenLake using Q-Learning
• CartPole using REINFORCE in PyTorch
• Cartpole in PyTorch
• Q-Learning on Lunar Lander and Frozen Lake
• REINFORCE
• Importance Sampling
• Kullback-Leibler Divergence
• MDP with Dynamic Programming in PyTorch
• REINFORCE in PyTorch
• MDP Basics with Inventory Control
• n-step algorithms and eligibility traces
• Q-Learning vs SARSA and Q-Learning extensions

RecSys Tutorials

• Multi-armed Bandit for Banner Ad
• Contextual Recommender with Vowpal Wabbit
• Top-K Off-Policy Correction for a REINFORCE Recommender System
• Neural Interactive Collaborative Filtering
• Batch-Constrained Deep Q-Learning
• Pydeep Recsys
• Recsim Catalyst
• Solving Multi-armed Bandit Problems
• Deep Reinforcement Learning in Large Discrete Action Spaces
• Off-Policy Learning in Two-stage Recommender Systems
• Comparing Simple Exploration Techniques: ε-Greedy, Annealing, and UCB
• Predicting rewards with the state-value and action-value function
• Real-Time Bidding in Advertising
• GAN User Model for RL-based Recommendation System