Proximal Policy Optimization

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

link to repository

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: “CNN vs LSTM - Reward function”

• 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.