Reinforcement_Learning

Material for MLT Reinforcement Learning workshops and study sessions.

Also, check out our MLT repo with top Deep RL resources (tutorials, code, books).

RL Interactive Tools

1. ε Decay
2. k-Armed Bandit
3. Exploration vs Explotation

• Original concept and Python code: Anugraha Sinha
• Javascript implementation: Francisco Dalla Rosa Soares

Intro to Reinforcement Learning – Session #1

by Anugraha Sinha

[Meetup] & [Slides and Code]

Presentation

1. Introduction to RL
2. Important elements of an RL problem
3. Description of Markov Decision Process (MDP) and and Markov Assumption.
4. Importance of parametrization of State, Action, Reward and Environment.
5. Model Based and Model Free Methods
6. Meaning of Control Problem and Evaluation Problem.
7. Algorithm of Policy Evaluation and Value iteration methods

Code examples

1. Finding the best route through a maze/obstruction avoidance using policy iteration algorithm.
2. Above problem statement with value iterations algorithm.
3. Code exercise