# How to handle changing goals in a DQN?

I created a virtual 2D environment where an agent aims to find a correct pose corresponding to a target image. I implemented a DQN to solve this task. When the goal is fixed, e.g. the aim is to find the pose for position (1,1), the agent is successful. I would now like to train an agent to find the correct pose while the goal pose changes after every episode. My research pointed me to the term "Multi-Objective Deep Reinforcement Learning". As far as I understood, the aim here is to train one or multiple agents to achieve a policy approximation that fits all goals. Am I on the right track or how should I deal with different goal states?

• Thanks for your explanation. So the state vector would maybe look like (...,current_pose, target_pose), where target_pose changes after every episode? I already included those information in my current implementation. I think my mistake was that I changed the target_pose after the agent found the previous one during my first tries. Very appreciated your help. Jun 23 '20 at 14:51
• Yes. If you can, then you should randomise target_episode according to expected distribution in production for each episode whilst training. If the initial pose can vary then you should randomise that too - again the ideal is to match distribution it would work with in production. Jun 23 '20 at 16:34