Anestis Fachantidis

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This article introduces a teacher-student framework for reinforcement learning, synthesizing and extending material that appeared in conference proceedings [22] and in a non-archival workshop paper [6]. In this framework, a teacher agent instructs a student agent by suggesting actions the student should take as it learns. However, the teacher may only give(More)
The main objective of Transfer Learning is to reuse knowledge acquired in a previous learned task, in order to enhance the learning procedure in a new and more complex task. Transfer learning comprises a suitable solution for speeding up the learning procedure in Reinforcement Learning tasks. This work proposes a novel method for transferring models to(More)
In this paper we investigate using multiple mappings for transfer learning in reinforcement learning tasks. We propose two different transfer learning algorithms that are able to manipulate multiple inter-task mappings for both model-learning and model-free reinforcement learning algorithms. Both algorithms incorporate mechanisms to select the appropriate(More)
The main objective of transfer learning is to reuse knowledge acquired in a previous learned task, in order to enhance the learning procedure in a new and more complex task. Transfer learning comprises a suitable solution for speeding up the learning procedure in Reinforcement Learning tasks. In this work, we propose a novel method for transferring models(More)
When transferring knowledge between reinforcement learning agents with different state representations or actions, past knowledge must be efficiently mapped to novel tasks so that it aids learning. The majority of the existing approaches use pre-defined mappings provided by a domain expert. To overcome this limitation and enable autonomous transfer(More)
Technological advancements in robotics and cognitive science are contributing to the development of the field of cognitive robotics. Modern robotic platforms are able to exhibit the ability to learn and reason about complex tasks and to follow behavioural goals in complex environments. Nevertheless, many challenges still exist. One of these great challenges(More)
In recent years, a variety of transfer learning (TL) methods have been developed in the context of reinforcement learning (RL) tasks. Typically, when an RL agent leverages TL, it uses knowledge acquired in one or more (source) tasks to speed up its learning in a more complex (target) task. When transferring knowledge between reinforcement learning agents(More)
In this paper we present the passenger demand prediction model of BusGrid. BusGrid is a novel information system for the improvement of productivity and customer service in public transport bus services. BusGrid receives and processes real time data from the automated vehicle location (AVL) and the automated passenger counting (APC) sensors installed on a(More)
This paper extends our existing teacher-student framework to allow a knowledgeable agent to teach human students. An agent teacher instructs a human student by suggesting actions the student should take as it learns. This paper extends previous algorithms, used for agents teaching other agents, to develop several new algorithms for agents teaching humans.(More)