Journal
NEW GENERATION COMPUTING
Volume 33, Issue 1, Pages 69-114Publisher
SPRINGER
DOI: 10.1007/s00354-015-0102-0
Keywords
Artificial Intelligence; Reinforcement Learning; Embodied Agent; Projective Simulation
Funding
- Austrian Science Fund (FWF) [SFB FoQuS F4012]
- Templeton World Charity Foundation (TWCF)
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We study the model of projective simulation (PS), a novel approach to artificial intelligence based on stochastic processing of episodic memory which was recently introduced. 2) Here we provide a detailed analysis of the model and examine its performance, including its achievable efficiency, its learning times and the way both properties scale with the problems' dimension. In addition, we situate the PS agent in different learning scenarios, and study its learning abilities. A variety of new scenarios are being considered, thereby demonstrating the model's flexibility. Furthermore, to put the PS scheme in context, we compare its performance with those of Q-learning and learning classifier systems, two popular models in the field of reinforcement learning. It is shown that PS is a competitive artificial intelligence model of unique properties and strengths.
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