Reinforcement learning techniques shows great potential for game-based AI but fails to scale on real-world applications. Indeed, state space and action space become continuous and, thus, prevent any tabular-based learning. Therefore, reinforcement learning agents require to be piloted by a learning decision function thus falling back on deep learning.
How reinforcement learning and deep learning team up to give birth to an efficient agent ?