Evolutionary algorithms have recently re-emerged as powerful tools for machine learning and artificial intelligence, especially when combined with advances in deep learning developed over the last decade. In contrast to the use of fixed architectures and rigid learning algorithms, we leveraged the open-endedness of evolutionary algorithms to make both theoretical and methodological contributions to deep reinforcement learning. This thesis explores and develops two major areas at the intersection of evolutionary algorithms and deep reinforcement learning: generative network architectures and behaviour-based optimization. Over three distinct contributions, both theoretical and experimental methods were applied to deliver a novel mathematical framework and experimental method for generative, modular neural network architecture search for reinforcement learning, and a generalized formulation of a behaviour- based optimization framework for reinforcement learning called novelty search. Experimental results indicate that both alternative, behaviour-based optimization and neural architecture search can each be used to improve learning in the popular Atari 2600 benchmark compared to DQN — a popular gradient-based method. These results are in-line with related work demonstrating that strictly gradient-free methods are competitive with gradient-based reinforcement learning. These contributions, together with other successful combinations of evolutionary algorithms and deep learning, demonstrate that alternative architectures and learning algorithms to those conventionally used in deep learning should be seriously investigated in an effort to drive progress in artificial intelligence
