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Deep Q-Learning has been successfully applied to a wide variety of tasks inthe past several years. However, the architecture of the vanilla Deep Q-Networkis not suited to deal with partially observable environments such as 3D videogames. For this, recurrent layers have been added to the Deep Q-Network inorder to allow it to handle past dependencies. We here use Minecraft for itscustomization advantages and design two very simple missions that can be framesas Partially Observable Markov Decision Process. We compare on these missionsthe Deep Q-Network and the Deep Recurrent Q-Network in order to see if thelatter, which is trickier and longer to train, is always the best architecturewhen the agent has to deal with partial observability.

Deep Recurrent Q-Learning vs Deep Q-Learning on a simple Partially Observable Markov Decision Process with Minecraft