This thesis presents an innovative method for generating tool path planning for the Wire Arc Additive Manufacturing (WAAM) process with reinforcement learning. The present state of the art provides numerous techniques to tackle this task. Most methods employ mathematical algorithms, while there are no instances of AI being used to completely design paths. The suggested solution seeks to train a reinforcement learning agent on diverse shapes, employing a custom reinforcement learning environment that simulates the WAAM process and a user-defined reward function that teaches the desired behavior of the path. Particular attention is given to the environment, which dictates the physical constraints that the agent is subjected to during its activity.