978 research outputs found
Benchmarking projective simulation in navigation problems
Projective simulation (PS) is a model for intelligent agents with a
deliberation capacity that is based on episodic memory. The model has been
shown to provide a flexible framework for constructing reinforcement-learning
agents, and it allows for quantum mechanical generalization, which leads to a
speed-up in deliberation time. PS agents have been applied successfully in the
context of complex skill learning in robotics, and in the design of
state-of-the-art quantum experiments. In this paper, we study the performance
of projective simulation in two benchmarking problems in navigation, namely the
grid world and the mountain car problem. The performance of PS is compared to
standard tabular reinforcement learning approaches, Q-learning and SARSA. Our
comparison demonstrates that the performance of PS and standard learning
approaches are qualitatively and quantitatively similar, while it is much
easier to choose optimal model parameters in case of projective simulation,
with a reduced computational effort of one to two orders of magnitude. Our
results show that the projective simulation model stands out for its simplicity
in terms of the number of model parameters, which makes it simple to set up the
learning agent in unknown task environments.Comment: 8 pages, 10 figure
Projective simulation with generalization
The ability to generalize is an important feature of any intelligent agent.
Not only because it may allow the agent to cope with large amounts of data, but
also because in some environments, an agent with no generalization capabilities
cannot learn. In this work we outline several criteria for generalization, and
present a dynamic and autonomous machinery that enables projective simulation
agents to meaningfully generalize. Projective simulation, a novel, physical
approach to artificial intelligence, was recently shown to perform well in
standard reinforcement learning problems, with applications in advanced
robotics as well as quantum experiments. Both the basic projective simulation
model and the presented generalization machinery are based on very simple
principles. This allows us to provide a full analytical analysis of the agent's
performance and to illustrate the benefit the agent gains by generalizing.
Specifically, we show that already in basic (but extreme) environments,
learning without generalization may be impossible, and demonstrate how the
presented generalization machinery enables the projective simulation agent to
learn.Comment: 14 pages, 9 figure
Smart Cities: Inverse Design of 3D Urban Procedural Models with Traffic and Weather Simulation
Urbanization, the demographic transition from rural to urban, has changed how we envision and share the world. From just one-fourth of the population living in cities one hundred years ago, now more than half of the population does, and this ratio is expected to grow in the near future. Creating more sustainable, accessible, safe, and enjoyable cities has become an imperative
Speeding-up the decision making of a learning agent using an ion trap quantum processor
We report a proof-of-principle experimental demonstration of the quantum
speed-up for learning agents utilizing a small-scale quantum information
processor based on radiofrequency-driven trapped ions. The decision-making
process of a quantum learning agent within the projective simulation paradigm
for machine learning is implemented in a system of two qubits. The latter are
realized using hyperfine states of two frequency-addressed atomic ions exposed
to a static magnetic field gradient. We show that the deliberation time of this
quantum learning agent is quadratically improved with respect to comparable
classical learning agents. The performance of this quantum-enhanced learning
agent highlights the potential of scalable quantum processors taking advantage
of machine learning.Comment: 21 pages, 7 figures, 2 tables. Author names now spelled correctly;
sections rearranged; changes in the wording of the manuscrip
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