Creating embodied agents capable of performing a wide range of tasks in different types of environments has been a longstanding challenge in deep reinforcement learning. A novel network architecture introduced in 2021 called the Active Dendrite Network [A. Iyer et al., “Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environments”] designed to create sparse subnetworks for different tasks showed promising multi-tasking performance on the Meta-World [T. Yu et al., “Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning”] multi-tasking benchmark. This thesis further explores the performance of this novel architecture in a multi-tasking environment focused on physical animations and locomotion. Specifically we implement and compare the architecture to the commonly used Multi-Layer Perceptron (MLP) architecture on a multi-task reinforcement learning problem in a video-game setting consisting of training a hexapedal agent on a set of locomotion tasks involving moving at different speeds, turning and standing still. The evaluation focused on two areas: (1) Assessing the average overall performance of the Active Dendrite Network relative to the MLP on a set of locomotive scenarios featuring our behaviour sets and environments. (2) Assessing the relative impact Active Dendrite networks have on transfer learning between related tasks by comparing their performance on novel behaviours shortly after training a related behaviour. Our findings suggest that the novel Active Dendrite Network can make better use of limited network capacity compared to the MLP - the Active Dendrite Network outperformed the MLP by ∼18% on our benchmark using limited network capacity. When both networks have sufficient capacity however, there is not much difference between the two. We further find that Active Dendrite Networks have very similar transfer-learning capabilities compared to the MLP in our benchmarks.