README.md

Bipedal Walker Evo

This project tries to solve OpenAI's bipedal walker using three different ways: Q-Learning, Mutation of Actions and Evolution Strategies.

Q-Learning

Coming soon

Action Mutation

Will get 0 reward, which is basically learning to prevent falling on it's head. The more actions the walker can use, the worse the reward. This is because the walker tries to generate movement by trembling with it's legs. The covered distance doesn't cover the punishment for doing actions. So after 1600 moves the walker will get a reward around -60.

How it works

1. Generate a population with a starting number randomized actions (we don't need enough actions to solve the problem right now)
2. Let the population play the game reward every walker of the generation accordingly
3. The best walker survives without mutating
4. The better the reward the higher the chance to pass actions to next generation. Each child has a single parent, no crossover.
5. Mutate all children and increment their number of actions

Hyperparameters

Parameter	Description	Interval	Our Choice
POP_SIZE	Size of population.	[0;∞[	50
MUTATION_FACTOR	Percentage of actions that will be mutated for each walker.	[0;1]	0.2
BRAIN_SIZE	Number of actions in the first generation.	[0;1600]	50
INCREASE BY	Incrementation of steps for each episode.	[0;∞[	5