In the field of Machine Learning (ML), Reinforcement Learning (RL) is the area that studies how to determine the actions that a software agent must take in different environments to maximize the reward it obtains. It explores how to combine and balance the exploration of new areas
and the use of the information the agent has about explored areas to achieve the best outcome.
In this project, I developed and tested the algorithms to implement several RL tasks in three different environments: a domain Maze with a discrete state space, and the Acrobot-v1 andMountainCar-v0 environments with a continuous state space from the RL toolkit OpenAI Gym. The algorithms I implemented are Policy Iteration, Q-Learning and REINFORCE with Baseline. I evaluated them to different hyperparameters and identified the optimal parameters.
If you want to learn more, check out the project repo below!
