Value Iteration Method - Reinforcement Learning
Value Iteration is an exact method of solving a Reinforcement Learning problem. The goal of the task is to find how much expected discounted reward we can get from s if we use the best possible policy.
In mathmetical notations, we calculate this(below equation) for every state in set of States S, given an MDP.
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| 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.
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| 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