Using Reinforcement Learning and Task Decomposition for Learning to Play Doom

Reinforcement learning (RL) is a basic machine learning method, which has recently gained in popularity. As the field matures, RL methods are being applied on progressively more complex problems. This leads to need to design increasingly more complicated models, which are difficult to train and apply in practice. This thesis explores one potential way of solving the problem with large and slow RL models, which is using a modular approach to build the models. The idea behind this approach is to decompose the main task into smaller subtasks and have separate modules each of which concentrates on solving a single subtask. In more detail, the proposed agent will be built using the Q-decomposition algorithm, which provides a simple and robust algorithm for building modular RL agents. The problem we use as an example of usefulness of the modular approach is a simplified version of the video game Doom and we design a RL agent that learns to play it. The empirical results indicate that the proposed model is able to learn to play the simplified version of Doom on a reasonable level, but not perfectly. Additionally, we show that the proposed model might suffer from usage of too simple models for solving the subtasks. Nevertheless, taken as a whole the results and the experience of designing the agent show that the modular approach for RL is a promising way forward and warrants further exploration

Exploratory Search of GANs with Contextual Bandits

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