Policy Iteration - Reinforcement Learning

Src: UC Berkley 2017 Deep RL bootcamp Lecture 1 slides

Task at Hand

The task is to maximize a reward in a world that consists of an agent that can navigate in 4 directions - North, South, East and West. With a 20% of equally likely chance of deviating to left or right from the action asked to perform.

Src: UC Berkley 2017 Deep RL bootcamp Lecture 1 slides

Usage

Modify main.json to suit your needs. The key names are self explanatory. Then run python main.py.

You can also create your own <user-defined>.json file with every paramter defined and then run python main.py --json_path <user-defined>.json