A Unifying Framework for Reinforcement Learning and Planning

Sequential decision making, commonly formalized as optimization of a Markov Decision Process, is a key challenge in artificial intelligence. Two successful approaches to MDP optimization are reinforcement learning and planning, which both largely have their own research communities. However, if both research fields solve the same problem,...

The Intersection of Planning and Learning

Intelligent sequential decision making is a key challenge in artificial intelligence. The problem, commonly formalized as a Markov Decision Process, is studied in two different research communities: planning and reinforcement learning. Departing from a fundamentally different assumption about the type of access to the environment, both research...

Emotion in reinforcement learning agents and robots: A survey

This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computational emotion models are...