Analysis of Proximal Policy Optimization algorithm Using OpenAI Gym

Goals:

• Comparing the algorithm performance with other baseline techniques for OpenAI game environment
• Exploring performance based on input data preprocessing , using different Neural Network architectures & CPU vs GPU training
• Modifying different hyperparameters to analyze their impact on the overall performance of the algorithm

Implementation:

• The model is developed using TensorFlow and input data is collected from OpenAI GYM's MS-PACMAN environment.

• Performance of different neural network architectures is explored:

• GPU based training was done using Google Collaboratory

• Reference : OpenAI GYM Baselines

Output:

• Different models based on the modified hyperparemeters, CPU training & GPU training.
• Performance comparison(rewards & loss function) plots.