Manavvora / Lunar_RL

Comparison of performance of different reinforcement learning algorithms applied to the lunar lander problem

dqn gym-environment lunar-lander monte-carlo qlearning reinforcement-learning reinforcement-learning-algorithms sarsa

AE504 Final Project

Authors: Gokul Puthumanaillam, Manav Vora

This is a code for comparing different algorithms to solve the lunar lander problem. The algorithms simulated are:

Monte Carlo
Q-Learning
DQN
SARSA

Outputs:

Simulation	Final Trajectory
Random policy
Monte Carlo
QLearning
SARSA
DQN

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Comparison of performance of different reinforcement learning algorithms applied to the lunar lander problem

dqn gym-environment lunar-lander monte-carlo qlearning reinforcement-learning reinforcement-learning-algorithms sarsa

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