Efficient exploration is a major issue in reinforcement learning, particularly in environments with sparse rewards. In these environments, traditional methods like e-greedy fail to efficiently reach an optimal policy. A new method proposed by Fortunato, et al. Fortunato, et al. showed promise by improving efficiency on RL tasks such as Atari games, by driving exploration with learned perturbations of the network weights. Three different types of settings were investigated in order to test the robustness of a contextual bandit implemented with this proposed method: 1. ContextualBandit-v2; a bandit with multiple predefined functions mapping the 1-dimensional continuous input to the reward, 2. MNISTBandit-v0; a bandit rewarding correct identification of MNIST dataset images, and 3. NNBandit-v0; a bandit with the reward being determined by a neural network. Furthermore, non-stationary variants of environment 1. and 3. were tested. A slight variation in hyperparameter sensitivity between environments was observed and a generally optimal set was determined. Overall, NoisyNet-DQNs (Deep Q-Networks) achieved performance comparable to regular DQNs, though often slightly lower. In the high-dimensional stationary MNISTBandit-v0 environment, NoisyNet-DQN converged to an optimal policy slightly faster, at the cost of a larger variation in performance.

Genome-wide association studies (GWAS) are commonly used to identify genetic variants associated with human traits by comparing genetic differences between diseased and healthy individuals. One way to gain insights into the biological consequences of these variants is to use quantitative trait locus (QTL) analysis. These connect SNP with variations in gene expression levels among individuals. QTL studies are mostly done on single nucleotide changes, but as SVs are bigger and have greater impact on traits, SV-QTL connections are of great interest. Using Gosling.js, a tool was developed to easily display the links and significance between SNPs, associated eQTLs, and SVs. The main purpose of this tool is to provide clear visualizations, while also offering options for further exploration of the chromosome. The existing search functionalities from snpXplorer have been integrated and enhanced. Users can define a variable window size before querying, allowing for flexible data examination. Additionally, the tool supports requests for data across multiple tissues. For improved performance and usability, the option to select which data tracks are shown were added.