General Tree Evaluation for AlphaZero

Over the last decade, there have been significant advances in model-based deep reinforcement learning. One of the most successful such algorithms is AlphaZero which combines Monte Carlo Tree Search with deep learning. AlphaZero and its successors commonly describe a unified framework for tree construction and acting. For instance, build the tree...

Combining Multi-Objective Planning with Reinforcement Learning to Solve Complex Tasks in Environments with Sparse Rewards

Sequential decision-making problems are problems where the goal is to find a sequence of actions that complete a task in an environment. A particularly difficult type of sequential decision-making problem to solve is one in which the environment has sparse rewards, a large state space, and where the goal is to complete a complex task. In this...

Modelling Agents with Variational Autoencoders in Multi-Agent Sequential Decision Making

The ability to model other agents can be of great value in multi-agent sequential decision making problems and has become more accessible due to the introduction of deep learning into reinforcement learning. In this study, the aim is to investigate the usefulness of modelling other agents using variational autoencoder based models in partially...

Scalable and Decentralized Algorithms for Anomaly Detection via Learning-Based Controlled Sensing

We address the problem of sequentially selecting and observing processes from a given set to find the anomalies among them. The decision-maker observes a subset of the processes at any given time instant and obtains a noisy binary indicator of whether or not the corresponding process is anomalous. We develop an anomaly detection algorithm that...

Generalized Models of Sequential Decision-Making under Uncertainty

Sequential decision-making under uncertainty is an important branch of artificial intelligence research with a plethora of real-life applications. In this thesis, we generalize two fundamental properties of the decision-making process. First, we show that the theory on planning methods for finite spaces can be extended to infinite but countable...

Sequential decision making using Bayesian networks and reinforcement learning: to determine the hydrocarbon prospect exploration strategy

In the process of drilling wells to produce hydrocarbons, an exploration strategy is used to determine which wells should be drilled and in which order. This strategy is vital, as a suboptimal drilling sequence will lead to more expenses and fewer gains.<br/>Furthermore, the wells considered in most exploration strategies are geologically<br/...

The Intersection of Planning and Learning

Intelligent sequential decision making is a key challenge in artificial intelligence. The problem, commonly formalized as a Markov Decision Process, is studied in two different research communities: planning and reinforcement learning. Departing from a fundamentally different assumption about the type of access to the environment, both research...

Environment Shift Games: Are Multiple Agents the Solution, and not the Problem, to Non-Stationarity?

Machine learning and artificial intelligence models that interact with and in an environment will unavoidably have impact on this environment and change it. This is often a problem as many methods do not anticipate such a change in the environment and thus may start acting sub-optimally. Although efforts are made to deal with this problem, we...

Timely condition-based maintenance planning for multi-component systems

Last-minute maintenance planning is often undesirable, as it may cause downtime during operational hours, may require rescheduling of other activities, and does not allow to optimize the management of spare parts, material, and personnel. In spite of the aforementioned drawbacks of last-minute planning, most existing methods plan maintenance...