Reinforcement Learning Tutorials

This repo contains project implementations in python that cover basics of Reinforcement Learning

1.Frozen Lake Implementation

    Concepts covered
       1. Model Based approach
       2. Dynamic Programming                                            
       3. Policy Iteration (Evaluation and Improvement)
       4. Value Iteration

2.Black Jack Implementation

    Concepts covered
       1. Model Free approach using Monte-Carlo methods
       2. Episodic Learning
       3. Monte-Carlo Prediction                                            
       4. Monte-Carlo Control
       5. Eplison Greedy method

3.Cliff Walking Implementation

    Concepts covered
       1. Model Free approach using Temporal Difference
       2. Sarsa for TD-Control                                            
       3. Q-learning for TD-Control

4.Atari Breakout

    Concepts covered
        1. Deep Q Learning