The recent successes of deep learning and deep reinforcement learning have firmly
established their statuses as state-of-the-art artificial learning techniques. However …

Building agents with general skills that can be applied in a wide range of settings has been
a long-standing problem in machine learning. The most popular framework for training …

Deep Reinforcement Learning (RL) has emerged as a powerful paradigm for training neural
policies to solve complex control tasks. However, these policies tend to be overfit to the …

Deep reinforcement learning (DRL) and evolution strategies (ESs) have surpassed human-
level control in many sequential decision-making problems, yet many open challenges still …

Recent advances in machine learning using deep neural networks have shown significant
successes in learning from large datasets. However, these successes concentrated on …

Reinforcement Learning (RL) has achieved impressive performance in many complex
environments due to the integration with Deep Neural Networks (DNNs). At the same time …

In recent years, there has been an increasing emphasis on developing generalist agents
capable of solving a diverse variety of tasks effectively. We hope that such an agent would …

In order perform a large variety of tasks and to achieve human-level performance in complex
real-world environments, Artificial Intelligence (AI) Agents must be able to learn from their …

Currently, Big Data techniques and Deep Learning are changing the way humankind
interacts with technology. From content recommendation to technologies capable of creating …

In this thesis we aim to improve generalisation in deep reinforcement learning.
Generalisation is a fundamental challenge for any type of learning, determining how …