Parallel distributed processing: explorations in the microstructure of cognition, vol. 2: psychological and biological models
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
Modeling motivations and emotions as a basis for intelligent behavior
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Artificial Life II
Agent-based modelling and the environmental complexity thesis
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
An Architecture for Behavior-Based Reinforcement Learning
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Actor-Critic Models of Reinforcement Learning in the Basal Ganglia: From Natural to Artificial Rats
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Modeling Behavior Cycles as a Value System for Developmental Robots
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Learning Affordances of Consummatory Behaviors: Motivation-Driven Adaptive Perception
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Self-Organizing Sensorimotor Maps Plus Internal Motivations Yield Animal-Like Behavior
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Valency for adaptive homeostatic agents: relating evolution and learning
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
An adaptive robot motivational system
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Cognitive Systems Research
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Reinforcement learning (RL) in the context of artificial agents is typically used to produce behavioral responses as a function of the reward obtained by interaction with the environment. When the problem consists of learning the shortest path to a goal, it is common to use reward functions yielding a fixed value after each decision, for example a positive value if the target location has been attained and a negative value at each intermediate step. However, this fixed strategy may be overly simplistic for agents to adapt to dynamic environments, in which resources may vary from time to time. By contrast, there is significant evidence that most living beings internally modulate reward value as a function of their context to expand their range of adaptivity. Inspired by the potential of this operation, we present a review of its underlying processes and we introduce a simplified formalization for artificial agents. The performance of this formalism is tested by monitoring the adaptation of an agent endowed with a model of motivated actor-critic, embedded with our formalization of value and constrained by physiological stability, to environments with different resource distribution. Our main result shows that the manner in which reward is internally processed as a function of the agent's motivational state, strongly influences adaptivity of the behavioral cycles generated and the agent's physiological stability.