In this thesis, we explore two distinct topics. The first part of the thesis delves into efficient
exploration in multi-task bandit models and model-free exploration in large Markov decision …

Exploration in unknown environments is a fundamental problem in reinforcement learning
and control. In this work, we study task-guided exploration and determine what precisely an …

The impetus for exploration in reinforcement learning (RL) is decreasing uncertainty about
the environment for the purpose of better decision making. As such, exploration plays a …

We study the problem of exploration in Reinforcement Learning and present a novel model-
free solution. We adopt an information-theoretical viewpoint and start from the instance …

Many potential applications of reinforcement learning (RL) are stymied by the large numbers
of samples required to learn an effective policy. This is especially true when applying RL to …

A key challenge in reinforcement learning is how an agent can efficiently gather useful
information about its environment to make the right decisions, ie, how can the agent be …

In many practical applications of RL, it is expensive to observe state transitions from the
environment. For example, in the problem of plasma control for nuclear fusion, computing …

We propose Deterministic Sequencing of Exploration and Exploitation (DSEE) algorithm
with interleaving exploration and exploitation epochs for model-based RL problems that aim …

Many real-world reinforcement learning tasks require control of complex dynamical systems
that involve both costly data acquisition processes and large state spaces. In cases where …

Abstract Model-based reinforcement learning is a powerful tool, but collecting data to fit an
accurate model of the system can be costly. Exploring an unknown environment in a sample …