Unified theories of cognition
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Meta-learning in reinforcement learning
Neural Networks
On Affect and Self-adaptation: Potential Benefits of Valence-Controlled Action-Selection
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Soar-RL: integrating reinforcement learning with Soar
Cognitive Systems Research
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
On Affect and Self-adaptation: Potential Benefits of Valence-Controlled Action-Selection
IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
Reinforcement learning as heuristic for action-rule preferences
ProMAS'10 Proceedings of the 8th international conference on Programming Multi-Agent Systems
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Reinforcement learning (RL) agents can benefit from adaptive exploration/exploitation behavior, especially in dynamic environments. We focus on regulating this exploration/exploitation behavior by controlling the action-selection mechanism of RL. Inspired by psychological studies which show that affect influences human decision making, we use artificial affect to influence an agent's action-selection. Two existing affective strategies are implemented and, in addition, a new hybrid method that combines both. These strategies are tested on `maze tasks' in which a RL agent has to find food (rewarded location) in a maze. We use Soar-RL, the new RL-enabled version of Soar, as a model environment. One task tests the ability to quickly adapt to an environmental change, while the other tests the ability to escape a local optimum in order to find the global optimum. We show that artificial affect-controlled action-selection in some cases helps agents to faster adapt to changes in the environment.