  Reinforcement Learning (RL) is an area of machine learning concerned with
enabling an agent to navigate an environment with uncertainty in order to
maximize some notion of cumulative long-term reward. In this paper, we
implement and analyze two different RL techniques, Sarsa and Deep QLearning, on
OpenAI Gym's LunarLander-v2 environment. We then introduce additional
uncertainty to the original problem to test the robustness of the mentioned
techniques. With our best models, we are able to achieve average rewards of
170+ with the Sarsa agent and 200+ with the Deep Q-Learning agent on the
original problem. We also show that these techniques are able to overcome the
additional uncertainities and achieve positive average rewards of 100+ with
both agents. We then perform a comparative analysis of the two techniques to
conclude which agent peforms better.
