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In reinforcement learning (RL), an agent makes sequential decisions to maximise the reward it can obtain from an environment. During learning, the actual and expected outcomes are compared to tell whether a decision was good or bad. The difference between ...
Learning to achieve one’s goal in a complex environment is a complicated task. In reinforcement learning (RL) tasks, an agent interacts with the environment to learn optimal actions. In humans, striatal areas are strongly involved in these tasks. During ag ...
Learning how to act and adapting to unexpected changes are remarkable capabilities of humans and other animals. In the absence of a direct recipe to follow in life, behaviour is often guided by rewarding and by surprising events. A positive or a negative o ...
Surprise-based learning allows agents to rapidly adapt to nonstationary stochastic environments characterized by sudden changes. We show that exact Bayesian inference in a hierarchical model gives rise to a surprise-modulated trade-off between forgetting o ...
Detection of curvilinear structures has long been of interest due to its wide range of applications. Large amounts of imaging data could be readily used in many fields, but it is practically not possible to analyze them manually. Hence, the need for automa ...
This chapter presents an overview of learning approaches for the acquisition of controllers and movement skills in humanoid robots. The term learning control refers to the process of acquiring a control strategy to achieve a task. While the definition is i ...
This work presents a fully distributed algorithm for learning the optimal policy in a multi-agent cooperative reinforcement learning scenario. We focus on games that can only be solved through coordinated team work. We consider situations in which K player ...
Buildings account for over 70% of the electricity use in the US. As cities grow, high peaks of electricity consumption are becoming more frequent, which leads to higher prices for electricity. Demand response is the coordination of electrical loads such th ...
For decades, neuroscientists and psychologists have observed that animal performance on spatial navigation tasks suggests an internal learned map of the environment. More recently, map-based (or model-based) reinforcement learning has become a highly activ ...
This chapter presents an overview of learning approaches for the acquisition of controllers and movement skills in humanoid robots. The term learning control refers to the process of acquiring a control strategy to achieve a task. While the definition is i ...
