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.