Objective: Using the AI_gym environment link design an algorithm which will instruct an agent to learn and succeed at different tasks.
What is Q-learning?
From Wikipedia: Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations.
For any finite Markov decision process (FMDP), Q-learning finds an optimal policy in the sense of maximizing the expected value of the total reward over any and all successive steps, starting from the current state. Q-learning can identify an optimal action-selection policy for any given FMDP, given infinite exploration time and a partly-random policy. "Q" refers to the function that the algorithm computes – the expected rewards for an action taken in a given state. link
link to video report: https://www.dropbox.com/s/94ji858p66gq7b0/openAI_video.mp4?dl=0
link to written report: https://steve303.github.io/AI_gym-MarkovDecisionProcess/openAI_report.pdf