You can change the selected game in interface.py and change things like the reward method in environment.py. Training a Reinforcement Learning agent with this is probably impossible with current techniques, but you're more than welcome to try.

TODO:

• Smarter implementation of the reset method
• Extracting more relevant game info: player coordinates, bullet coordinates list, enemy coordinates list...
• Adding parameters to select the observation space (single frame/buffer (with specific size), with/without game variables, with/without visual input...)
• Better documentation & code clean-up
• Re-write interface.py as a DLL for cleaner game interaction
• Custom policy for training with visual and game variable data
• Manage to train a model that at least learns not to bomb every other frame...