Generalization and sample efficiency have been long-standing issues concerning
reinforcement learning, and thus the field of Offline Meta-Reinforcement Learning (OMRL) …

Multi-task reinforcement learning endeavors to accomplish a set of different tasks with a
single policy. To enhance data efficiency by sharing parameters across multiple tasks, a …

Exploring the design space is a critical aspect of engineering and optimization, involving the
search for the best configuration in complex systems with numerous options. In the system …

Access control is a fundamental security mechanism for computer systems, acting as the first
line of defense against potential threats. Its primary objective is to prevent unauthorized …

Multi-task reinforcement learning endeavors to efficiently leverage shared information
across various tasks, facilitating the simultaneous learning of multiple tasks. Existing …

This thesis presents a novel approach for training multi-agent reinforcement learning
(MARL) agents that are robust to different unforeseen gameplay strategies in sequential …

A large number of reinforcement learning (RL) environments are available to the research
community. However, due to differences across these environments, it is difficult to transfer …

Current multi-task reinforcement learning (MTRL) methods have the ability to perform a large
number of tasks with a single policy. However when attempting to interact with a new …

In the thesis, we set out to tackle the issue of Goal Misgeneralization (GMG) in Sequential
Decision Making (SDM) 1 by focusing on improving task specification. Below, we first link …