Reinforcement Learning
My first project below is my implementation of "An introduction to Reinforcement Learning", split into two, following the structure of the book.
My implementation of Richard Sutton & Andrew Barto's book, An Introduction to Reinforcement Learning.
I Tabular Solution Methods
- Ch 2.
Multi-armed Bandits - Ch 3, 4.
Finite Markov Decision Processes, Dynamic Programming - Ch 5.
Monte Carlo Methods - Ch 6.
Temporal-Difference Learning - Ch 7.
n-step Bootstrapping - Ch 8.
Planning and Learning with Tabular Methods
- This included the intuitions and prerequsite Reinforcement Learning knowledge for the algorithms in the next section that can be applied in an uncountable number of ways to interesting real life problems.
II Approximate Solution Methods
- Ch 9.
On-policy Prediction with Approximation - Ch 10.
On-policy Control with Approximation - Ch 11.
Off-policy Methods with Approximation - Ch 12.
Eligibility Traces - Ch 13.
Policy Gradient Methods
- This included the intuitions and prerequsite Reinforcement Learning knowledge for the algorithms in the next section that can be applied in an uncountable number of ways to interesting real life problems.