# Solving The Lunar Lander Problem under Uncertainty using Reinforcement Learning

arXiv: https://arxiv.org/abs/2011.11850 · HTML: https://mlsanity.com/paper/2011.11850/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Constrained Policy Gradient Method for Safe and Fast Reinforcement Learning: a Neural Tangent Kernel Based Approach](https://mlsanity.com/paper/2107.09139/) (2021)
- [Deep Inductive Logic Programming meets Reinforcement Learning](https://mlsanity.com/paper/2308.16210/) (2023)
- [Beyond Average Performance: Dynamic Instance Clustering and Specialized Algorithm Design in LLM-Assisted Evolutionary Search](https://mlsanity.com/paper/2608.03129/) (2026)

## Abstract (arXiv)

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.