BACKGROUND Abnormalities in reinforcement learning and reversal learning have been reported in psychosis, possibly secondary to subcortical dopamine abnormalities. METHODS We studied simple discrimination (SD) learning and reversal learning in a sample of 119 first-episode psychosis patients from the Cambridge early psychosis service (CAMEO) and 107 control participants. We used data on reinf...

Reinforcement learning (RL) is a biologically supported learning paradigm, which allows an agent to learn through experience acquired by interaction with its environment. Its potential to learn complex action sequences has been proven for a variety of problems, such as navigation tasks. However, the interactive randomized exploration of the state space, common in reinforcement learning, makes i...

Relational reinforcement learning is a promising direction within reinforcement learning research. It upgrades reinforcement learning techniques by using relational representations for states, actions, and learned value-functions or policies to allow natural representations and abstractions of complex tasks. Multiagent systems are characterized by their relational structure and present a good e...

In this paper we survey the basics of Reinforcement Learning and (Evolutionary) Game Theory, applied to the field of Multi-Agent Systems. This paper contains three parts. We start with an overview on the fundamentals of Reinforcement Learning. Next we summarize the most important aspects of Evolutionary Game Theory. Finally, we discuss the state-of-the-art of Multi-Agent Reinforcement Learning ...

Despite many promising results from the use of reinforcement learning in simulated robot worlds, its use in real robot worlds is relatively rare. This paper addresses challenges related to real robot worlds and shows how reinforcement learning combined with linear function approximation can solve many of them. Experiments are performed using a light-seeking robot built with the Lego Mindstorms ...

In the thesis we propose, we focus on equipping existing Reinforcement Learning algorithms with different kinds of safety constraints imposed on the exploration scheme. Common Reinforcement Learning algorithms are (sometimes implicitly) assumed to work in an ergodic1, or even “restartable” environment. However, these conditions are not achievable in field robotics, where the expensive robots ca...

Scaling reinforcement learning methods to large, challenging decision making tasks can potentially benefit from integrating domain specific knowledge in a principled manner. This synthesis focuses on applying two forms of domain knowledge about the game of Go to improve learning performance on what continues to be an extremely challenging task. First, learning is bootstrapped by using reinforce...

This paper proposes an algorithm to convert a T -stage stochastic decision problem with a continuous state space to a sequence of supervised learning problems. The optimization problem associated with the trajectory tree and random trajectory methods of Kearns, Mansour, and Ng, 2000, is solved using the Gauss-Seidel method. The algorithm breaks a multistage reinforcement learning problem into a...

Reinforcement learning has shown promise in learning policies that can solve complex problems. However, manually specifying a good reward function can be difficult, especially for intricate tasks. Inverse reinforcement learning offers a useful paradigm to learn the underlying reward function directly from expert demonstrations. Yet in reality, the corpus of demonstrations may contain trajectori...

This paper presents a hybrid dynamic control approach to the realisation of humanoid biped robotic walk, focusing on the policy gradient episodic reinforcement learning with fuzzy evaluative feedback. The proposed structure of controller involves two feedback loops: a conventional computed torque controller and an episodic reinforcement learning controller. The reinforcement learning part inclu...