Bird only jumps if that value is greater than 1/2. A normalized value between 0 and 1 is outputed by the neural network. In short, all we need to do is to feed the observations, manage the output actions and give reward points if the bird agent is in a good position or remove some if it dies by hitting an obstacle. The ML-Agents Plugin works with the Tensorflow Machine Learning Library to generate the neural network during the training session. Outputs are calculated by the neural network based on reward points given to the agent depending on if it did something good. Birds (or learning agents) are subject to a training session where inputs (or observations) are processed through a neural network that will output the user action, which in this case, is to flap the bird's wing to jump up. This time, I went with reinforcement learning. The Unity ML-Agents Plugin provides tools for several Machine Learning methods: reinforcement learning, imitation learning and neuroevolution. It turned out pretty great so here's a summary of my implementation.

I gave it a try by making an AI that taught itself how to play a Flappy Bird kind of game using neural networks and reinforcement learning. In 2017, Unity released the ML-Agents Plugin to help developers integrate Machine Learning into their game. Machine Learning concepts can be used as powerful tools by game developers to create NPC behaviours, help balance game mechanics or automate quality assurance.

Parkitect game mechanics