Rl

Reinforcement learning (RL) is a way of training an agent to make sequences of decisions by finding the optimal action-taking policy through trial and error interactions with the environment. It is a powerful tool for solving complex problems in robotics, finance, and gaming. To broaden my knowledge in ML, I worked on a few small projects to learn fundamental deep RL algorithms such as (Double) DQN, REINFORCE, DDPG, and PPO. Please see this project if you are interested in NEAT and Pong instead.

NEAT, or NeuroEvolution of Augmenting Topologies, is a genetic algorithm for evolving neural networks. Hayson and I used the neat-python library to train a neural network to play Pong as an optional course project to develop an AI that compete in Pong tournament. The agent maximizes the reward by learning to hit the ball back to the opponent.

Checkout Hayson’s amazing blog post where he explained the theory of how NEAT works!