Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Metalearning and neuromodulation
Neural Networks - Computational models of neuromodulation
Meta-learning in reinforcement learning
Neural Networks
Strategies for Affect-Controlled Action-Selection in Soar-RL
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Strategies for Affect-Controlled Action-Selection in Soar-RL
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Guidelines for Designing Computational Models of Emotions
International Journal of Synthetic Emotions
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Psychological studies have shown that emotion and affect influence learning. We employ these findings in a machine-learning meta-parameter context, and dynamically couple an adaptive agent's artificial affect to its action-selection mechanism (Boltzmann β). The agent's performance on two important learning problems is measured. The first consists of learning to cope with two alternating goals. The second consists of learning to prefer a later larger reward (global optimum) for an earlier smaller one (local optimum). Results show that, compared to several control conditions, coupling positive affect to exploitation and negative affect to exploration has several important benefits. In the alternating-goal task, it significantly reduces the agent's "goal-switch search peak". The agent finds its new goal faster. In the second task, artificial affect facilitates convergence to a global instead of a local optimum, while permitting to exploit that local optimum. We conclude that affect-controlled action-selection has adaptation benefits.