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I'm quite a newbie when it comes to practically working with Deep Learning techniques, although I studied them quite a lot theoretically in the last months. However, now I'm facing my first practical task.

The setting is the following: There is an air hockey game that exists both physically and as a simulation which could be run in parallel. Currently, the robotic opponent is programmed with a fixed algorithm and should further on be replaced by a trainable AI. We are not operating in a high-dimensional space (such as video frames) as the environment is solely represented as a two-dimensional surface (x and y coordinates of the mallets and the puck).

Following my research, I decided to go for an approach incorporating Model-Based Reinforcement Learning in order to achieve a descent sample efficiency as the simulation won't run much faster than real time. Therefore, I plan to pre-train the network using classic Supervised Learning methods with data generated by the already existing simulation to learn about the environment's model beforehand.

As I'm lacking some practical knowledge and I read about several problems regarding the convergence of deep Q-learning methods, I'm not sure how to proceed with the Reinforcement Learning part. Also, I'm pretty overwhelmed by the publications made in this field throughout the last years. Thus, I would appreciate some practical recommendations, which kind of algorithms (specific policy gradient, Q-learning or hybrid methods) might be promising for my application.

Thank you in advance!

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Air hockey is a more complicated version of the pong game. The player is able to move the racket in more directions and this allows a better control over the ball. Model based-learning is equal to store domain knowledge into the Artificial Intelligence. This is done by splitting the domain into subtasks. It make sense to divide the problem into two subproblems: “approaching the ball” and “hit the ball”.

Both problems have to be trained separately by a reinforcement learning algorithm. It's done by analyzing the input-output relations. Features for the racket position, the ball position, the direction and the opponent's position are forming the learning space to hold the policy. Because it's a beginner project, a normal black box neural network for storing the q-table values works great. The neural network gets trained to store the numerical values of the features.

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