Matteo Melloni@matteomelloni

Applied reinforcement learning for agent to play these 3 games: Cartpole, LunarLander, BiPedal Walker. We used the basic model of Environment -> State -> Agent -> Action to train our agent. We reward the agent for achieving an outcome that we want, while penalizing the agent for doing otherwise. After many iterations, our agents learns to clear the games.

Are you interested in learning more about reinforcement learning in artificial intelligence, but you are not sure where to start? If this sounds like something that interests you, then this hackathon event is for you!