Evoman is a video game playing framework inspired on Megaman.

A demo can be found here: https://www.youtube.com/watch?v=ZqaMjd1E4ZI

Reinforcement Learning Extension

This branch of the EvoMan environment introduces two OpenAI Gym Environment adaptation of the original EvoMan Environment. The adaptations are identical save for the input. gym_environment takes 5 binary inputs, which gym_environment_discrete takes a single input between 0 and 31. The discrete input is then split into 5 binary inputs. This is introduced to deal with the limitations of some Reinforcement Learning Algorithms. For this discrete input, it is important to realise that actions which are similar number-wise are not necessarily similar when it comes to the performed move.

A description of OpenAI's Gym environments can be found at https://gym.openai.com/. A library which uses OpenAI Gym environments can be found at https://github.com/DLR-RM/stable-baselines3.