Comparative Analysis of Exploration Algorithms in Deep Reinforcement Learning for Autonomous Driving: How does epsilon-greedy, random network distillation, bootstrapped DQN affect training and the robustness of final policies under various testing conditions in autonomous driving?

Autonomous driving is a rapidly evolving field that aims to enhance road safety and reduce accidents through the use of advanced software and hardware technologies. Reinforcement learning (RL) combined with deep neural networks has emerged as a promising approach for training autonomous agents. This research paper investigates three exploration...

Improvements in Monte Carlo Tree Search for Inductive Program Synthesis

A recent development in program synthesis is using Monte Carlo Tree Search to traverse the search tree of possible programs in order to efficiently find a program that will successfully transform the given input to the desired output. Previous research has shown promising results as Monte Carlo Tree Search is able to escape local optima that...

Comparing Exploration Approaches in Deep Reinforcement Learning for Traffic Light Control

Identifying the most efficient exploration approach for deep reinforcement learning in traffic light control is not a trivial task, and can be a critical step in the development of reinforcement learning solutions that can effectively reduce traffic congestion. It is common to use baseline dithering methods such as $\epsilon$-greedy. However,...

A Systematic Design Space Exploration of Datacenter Schedulers

Datacenter infrastructure has become vital for stakeholders across industry, academia and government. To operate efficiently, datacenter operators rely on a variety of complex scheduling techniques, to distribute user workloads across resources. In this work, we leverage a reference architecture for datacenter scheduling to design and implement...