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.pyto start training the agent - Set
viewOnly = Trueto load saved neural network weights and render results from trained agent
- Run
- 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
