CS7642 Project 2: OpenAI’s Lunar Lander problem, an 8-dimensional state space and 4-dimensional action space problem. The goal was to create an agent that can guide a space vehicle to land autonomously in the environment without crashing.

This is an implementation of Double Deep Q-learning with experience replay trained with 5000 epochs.

pip install keras gym[all] numpy

• main.py is the reinforcement learning agent
  • Run python main.py to start training the agent
  • Set viewOnly = True to load saved neural network weights and render results from trained agent

• Trained agent model is saved in the weights/ directory as trained_agent.h5
• Trained agent result (reward vs episode) is saved in the results/ directory as trained_agent.txt
• Hyperparameter search results (reward vs episode) is saved in the results/ directory as files in alpha search and gamma search
• Performance graphs are found in the graphs/ directory