Implemented Deep Q-Learning and Policy Gradient methods for Atari Games using PyTorch and OpenAI Gym along with various classical RL methods using Numpy such as Dynamic Programming (Policy and Value iteration), Monte Carlo (Epsilon-greedy and off-policy), TD Learning (Q-Learning and SARSA) and Q-Learning with Function Approximation. Presently studying state of the art variants of actor-critic methods.