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- Vassilis Vassiliades, Chris Christodoulou
- The 2010 International Joint Conference on Neural…
- 2010

In this paper, we investigate the importance of rewards in Multiagent Reinforcement Learning in the context of the Iterated Prisoner's Dilemma. We use an evolutionary algorithm to evolve valid payoff structures with the aim of encouraging mutual cooperation. An exhaustive analysis is performed by investigating the effect of: i) the lower and upper bounds of… (More)

This paper investigates Multiagent Reinforcement Learning (MARL) in a general-sum game where the payoffs’ structure is such that the agents are required to exploit each other in a way that benefits all agents. The contradictory nature of these games makes their study in multiagent systems quite challenging. In particular, we investigate MARL with spiking… (More)

- Vassilis Vassiliades, Aristodemos Cleanthous, Chris Christodoulou
- IEEE Transactions on Neural Networks
- 2011

This paper investigates multiagent reinforcement learning (MARL) in a general-sum game where the payoffs' structure is such that the agents are required to exploit each other in a way that benefits all agents. The contradictory nature of these games makes their study in multiagent systems quite challenging. In particular, we investigate MARL with spiking… (More)

The high probability of hardware failures prevents many advanced robots (e.g., legged robots) from being confidently deployed in real-world situations (e.g., post-disaster rescue). Instead of attempting to diagnose the failures, robots could adapt by trial-and-error in order to be able to complete their tasks. In that case, damage recovery can be seen as a… (More)

Successful protein secondary structure prediction is an important step towards modelling protein 3D structure, with several practical applications. Even though in the last four decades several PSSP algorithms have been proposed, we are far from being accurate. The Bidirectional Recurrent Neural Network (BRNN) architecture of Baldi et al. [1] is currently… (More)

The most data-efficient algorithms for reinforcement learning (RL) in robotics are based on uncertain dynamical models: after each episode, they first learn a dynamical model of the robot, then they use an optimization algorithm to find a policy that maximizes the expected return given the model and its uncertainties. It is often believed that this… (More)

The recently introduced Multi-dimensional Archive of Phenotypic Elites (MAP-Elites) is an evolutionary algorithm capable of producing a large archive of diverse, high-performing solutions in a single run. It works by discretizing a continuous feature space into unique regions according to the desired discretization per dimension. While simple, this… (More)

- Vassilis Vassiliades, Chris Christodoulou
- Neural Computation
- 2013

We consider the problem of designing local reinforcement learning rules for artificial neural network (ANN) controllers. Motivated by the universal approximation properties of ANNs, we adopt an ANN representation for the learning rules, which are optimized using evolutionary algorithms. We evaluate the ANN rules in partially observable versions of four… (More)

The recently introduced Intelligent Trial-and-Error (IT&E) algorithm showed that robots can adapt to damage in a matter of a few trials. The success of this algorithm relies on two components: prior knowledge acquired through simulation with an intact robot, and Bayesian optimization (BO) that operates on-line, on the damaged robot. While IT&E leads to fast… (More)

- V C Vassiliades, M E Bernardino
- Topics in magnetic resonance imaging : TMRI
- 1990

Hepatic MRI has become a useful imaging modality in recent years. As with all diagnostic imaging, the scanning techniques available for hepatic MRI are manifold and complex. The article discusses these techniques and their practical applications. Contrast agents and the MR appearances of various pathologic hepatic entities are also reviewed.