The present study investigated how stress affects instrumental learning performance in horses (Equus caballus) depending on the type of reinforcement. Horses were assigned to four groups (N = 15 per group); each group received training with negative or positive reinforcement in the presence or absence of stressors unrelated to the learning task. The instrumental learning task consisted of the h...

We present an algorithm based on reinforcement and state recurrence learning techniques to solve control scheduling problems. In particular, we have devised a simple learning scheme called "handicapped learning", in which the weights of the associative search element are reinforced, either positively or negatively, such that the system is forced to move towards the desired setpoint in the short...

Demands in the Ultimatum Game in its traditional form with one proposer and one responder are compared with demands in an Ultimatum Game with responder competition. In this modified form one proposer faces three responders who can accept or reject the split of the pie. Initial demands in both ultimatum games are quite similar, however in the course of the experiment, demands in the ultimatum ga...

The number of proposed reinforcement learning algorithms appears to be ever-growing. This article tackles the diversification by showing a persistent principle in several independent reinforcement learning algorithms that have been applied to multi-agent settings. While their learning structure may look very diverse, algorithms such as Gradient Ascent, Cross learning, variations of Q-learning a...

One of the very interesting properties of Reinforcement Learning algorithms is that they allow learning without prior knowledge of the environment. However, when the agents use algorithms that enable a generalization of the learning, they are unable to explain their choices. Neural networks are good examples of this problem. After a reminder about the basis of Reinforcement Learning, the Lattic...

Reinforcement learning has been widely used in explaining animal behavior. In reinforcement learning, the agent learns the value of the states in the task, collectively constituting the task state space, and uses the knowledge to choose actions and acquire desired outcomes. It has been proposed that the orbitofrontal cortex (OFC) encodes the task state space during reinforcement learning. Howev...

In this paper we study Relational Reinforcement Learning in a multi-agent setting. There is growing evidence in the Reinforcement Learning research community that a relational representation of the state space has many benefits over a propositional one. Complex tasks as planning or information retrieval on the web can be represented more naturally in relational form. Yet, this relational struct...

This paper investigates the potential of flat and hierarchical reinforcement learning (HRL) for solving problems within strategy games. A HRL method, Max-Q, is applied to a unit transportation task modelled within a simplified, discrete real-time strategy game engine, and its performance compared to that of flat Q-learning. It is shown that reinforcement learning approaches, and especially hier...

Relational reinforcement learning combines traditional reinforcement learning with a strong emphasis on a relational (rather than attribute-value) representation. Earlier work used relational reinforcement learning on a learning version of the classic Blocks World planning problem (a version where the learner does not know what the result of taking an action will be). “Structural” learning resu...