Reinforcement learning with replacing eligibility traces
Machine Learning - Special issue on reinforcement learning
Learning to solve multiple goals
Learning to solve multiple goals
How to dynamically merge Markov decision processes
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Modeling embodied visual behaviors
ACM Transactions on Applied Perception (TAP)
Research Issues in Multiple Policy Optimization Using Collaborative Reinforcement Learning
SEAMS '07 Proceedings of the 2007 International Workshop on Software Engineering for Adaptive and Self-Managing Systems
Towards adaptive programming: integrating reinforcement learning into a programming language
Proceedings of the 23rd ACM SIGPLAN conference on Object-oriented programming systems languages and applications
On the difficulty of modular reinforcement learning for real-world partial programming
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Dynamic scheduling of maintenance tasks in the petroleum industry: A reinforcement approach
Engineering Applications of Artificial Intelligence
The scared robot: motivations in a simulated robot arm
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Extending adaptive fuzzy behavior hierarchies to multiple levels of composite behaviors
Robotics and Autonomous Systems
Self-Organizing Sensorimotor Maps Plus Internal Motivations Yield Animal-Like Behavior
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Multi-policy optimization in self-organizing systems
SOAR'09 Proceedings of the First international conference on Self-organizing architectures
An adaptive robot motivational system
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Modeling the brain's operating system
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
A distributed Q-learning approach for variable attention to multiple critics
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Object focused q-learning for autonomous agents
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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We present a new algorithm, GM-Sarsa(O), for finding approximate solutions to multiple-goal reinforcement learning problems that are modeled as composite Markov decision processes. According to our formulation different sub-goals are modeled as MDPs that are coupled by the requirement that they share actions. Existing reinforcement learning algorithms address similar problem formulations by first finding optimal policies for the component MDPs, and then merging these into a policy for the composite task. The problem with such methods is that policies that are optimized separately may or may not perform well when they are merged into a composite solution. Instead of searching for optimal policies for the component MDPs in isolation, our approach finds good policies in the context of the composite task.