Skip to content

Model based learning + various model free based learning examples

Notifications You must be signed in to change notification settings

janrth/reinforcement_learning

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

28 Commits
 
 
 
 
 
 
 
 

Repository files navigation

Different types of reinforcement learnings will be covered here.

I begin with model based learning and show solutions for FrozenLake using OpenAI gym using value iteration and policy iteration.

Conceptually in value iteration we do the following:

  • compute the optimal value function first for each state-action pair
  • extract the optimal policy from the optimal value function

While using the policy iteration method we do the following:

  • start with a random policy
  • compute the value function
  • extract a new policy using the value function from the previous step
  • compare the old and the new policy and stop if the difference between them is below a threshold, otherwise continue with another policy (and compute the value function again)

For model free based problems, solutions for Monte Carlo prediction methods, temporal differencing (e.g.: Q Learning), multi armed banidts and deep q network are implemented. The different models are tested in jupyter notebooks where results can be observed.

About

Model based learning + various model free based learning examples

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published