Policy Gradient Methods in Reinforcement Learning

This is a report on the use of policy gradient methods in Reinforcement Learning. The focus of this report is on the experimental comparison between 3 types of Actor-Critic methods. This investigation mainly revolves around the effect of the eligibility traces on Actor-Critic methods. These methods are the followings:

1. Actor-Critic with Eligibility Traces
2. Actor-Critic with Eligibility Traces only on the Critic but not the Actor
3. Actor-Critic Without any Eligibility Traces using one-step returns

The experiments are carried on the FrozenLake environment.

• This entire report was done using Google Colaboratory and you can view the ipython notebook here.