Adaptive Behavior
Multiple paired forward and inverse models for motor control
Neural Networks - Special issue on neural control and robotics: biology and technology
Reinforcement learning with hierarchies of machines
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Neural Networks - Special issue on organisation of computation in brain-like systems
Multiple paired forward-inverse models for human motor learning and control
Proceedings of the 1998 conference on Advances in neural information processing systems II
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Advances in Neural Information Processing Systems 5, [NIPS Conference]
MOSAIC Model for Sensorimotor Learning and Control
Neural Computation
Reinforcement Learning in Continuous Time and Space
Neural Computation
Inter-module credit assignment in modular reinforcement learning
Neural Networks
Actor-Critic Models of Reinforcement Learning in the Basal Ganglia: From Natural to Artificial Rats
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Motor primitive and sequence self-organization in a hierarchical recurrent neural network
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Dealing with non-stationary environments using context detection
ICML '06 Proceedings of the 23rd international conference on Machine learning
A Model of Prefrontal Cortical Mechanisms for Goal-directed Behavior
Journal of Cognitive Neuroscience
Improving reinforcement learning with context detection
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Neural Networks - 2006 Special issue: Neurobiology of decision making
Self-organized Reinforcement Learning Based on Policy Gradient in Nonstationary Environments
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Modular Neural Networks for Model-Free Behavioral Learning
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Opportunities for multiagent systems and multiagent reinforcement learning in traffic control
Autonomous Agents and Multi-Agent Systems
Basal Ganglia Models for Autonomous Behavior Learning
Creating Brain-Like Intelligence
A Two-Level Model of Anticipation-Based Motor Learning for Whole Body Motion
Anticipatory Behavior in Adaptive Learning Systems
2009 Special Issue: Explorations on artificial time perception
Neural Networks
RL-CD: dealing with non-stationarity in reinforcement learning
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Development of Symbiotic Brain-Machine Interfaces Using a Neurophysiology Cyberworkstation
Proceedings of the 13th International Conference on Human-Computer Interaction. Part II: Novel Interaction Methods and Techniques
Intelligence Dynamics: a concept and preliminary experiments for open-ended learning agents
Autonomous Agents and Multi-Agent Systems
Levels and Types of Action Selection: The Action Selection Soup
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Hierarchical Architecture with Modular Network SOM and Modular Reinforcement Learning
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Constructing action set from basis functions for reinforcement learning of robot control
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Reinforcement learning of multiple tasks using parametric bias
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Time perception in shaping cognitive neurodynamics of artificial agents
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Switching between different state representations in reinforcement learning
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
Prerequesites for symbiotic brain-machine interfaces
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Neuronal replicators solve the stability-plasticity dilemma
Proceedings of the 12th annual conference on Genetic and evolutionary computation
The neuronal replicator hypothesis
Neural Computation
eMOSAIC model for humanoid robot control
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Mosaic for multiple-reward environments
Neural Computation
An online adaptation control system using mnSOM
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
The eMOSAIC model for humanoid robot control
Neural Networks
Q-error as a selection mechanism in modular reinforcement-learning systems
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
DCOB: Action space for reinforcement learning of high DoF robots
Autonomous Robots
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We propose a modular reinforcement learning architecture for nonlinear, nonstationary control tasks, which we call multiple model-based reinforcement learning (MMRL). The basic idea is to decompose a complex task into multiple domains in space and time based on the predictability of the environmental dynamics. The system is composed of multiple modules, each of which consists of a state prediction model and a reinforcement learning controller. The "responsibility signal," which is given by the softmax function of the prediction errors, is used to weight the outputs of multiple modules, as well as to gate the learning of the prediction models and the reinforcement learning controllers. We formulate MMRL for both discrete-time, finite-state case and continuous-time, continuous-state case. The performance of MMRL was demonstrated for discrete case in a nonstationary hunting task in a grid world and for continuous case in a nonlinear, nonstationary control task of swinging up a pendulum with variable physical parameters.