Projective simulation for classical learning agents: a comprehensive investigation

Abstract

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 [H.J. Briegel and G. De las Cuevas. Sci. Rep. 2, 400, (2012)]. 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.

Accepted for publication in New Generation Computing. 23 pages, 23 figures

Cited by in corpus (28)

• Active learning machine learns to create new quantum experiments
• Optimizing Quantum Error Correction Codes with Reinforcement Learning
• Machine learning for long-distance quantum communication
• Quantum Machine Learning: from physics to software engineering
• Measurement-based adaptation protocol with quantum reinforcement learning
• Adaptive quantum computation in changing environments using projective simulation
• Machine learning \& artificial intelligence in the quantum domain
• Projective simulation with generalization
• Setting up experimental Bell test with reinforcement learning
• Meta-learning within Projective Simulation
• Modelling collective motion based on the principle of agency
• Speeding-up the decision making of a learning agent using an ion trap quantum processor
• Collective defense of honeybee colonies: experimental results and theoretical modeling
• Benchmarking projective simulation in navigation problems
• Development of swarm behavior in artificial learning agents that adapt to different foraging environments
• Faster quantum mixing for slowly evolving sequences of Markov chains
• On the convergence of projective-simulation-based reinforcement learning in Markov decision processes
• Quantum machine learning with glow for episodic tasks and decision games
• Learning how to find targets in the micro-world: The case of intermittent active Brownian particles
• Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning
• Optimal foraging strategies can be learned
• A model of interacting quantum neurons with a dynamic synapse
• Can biological quantum networks solve NP-hard problems?
• A Projective Simulation Scheme for Partially-Observable Multi-Agent Systems
• Demonstration of quantum projective simulation on a single-photon-based quantum computer
• Run-and-Tumble Particles Learning Chemotaxis
• Quantum circuit synthesis of Bell and GHZ states using projective simulation in the NISQ era
• Multi-Excitation Projective Simulation with a Many-Body Physics Inspired Inductive Bias