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"Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms."

Introduction -- Classical and Quantum Ideal Gases -- Gross-Pitaevskii Model of the Condensate -- Waves and Solitons -- Superfluid Helium -- Vortices and Rotation -- Two-component, Dipolar and Polariton Condensates -- Quantum droplets -- Vortex dynamics in two and three dimensions -- Two-dimensional turbulence -- Three dimensional turbulence -- Appendix A: Simulating the 1D GPE with Matlab -- Appendix B: Simulating the 2D GPE with Python -- Subject index

Preface -- Introduction -- Quantum fluids -- A system of noninteracting particles ideal gas -- Bec of interacting particles -- Experiments in a nutshell -- Path integrals for bosons -- Optimized perturbation theory -- Bec in optimal perturbation theory -- Tans contact in mean field theories -- The condensate phase and its relation to the anomalous density -- Two component bose mixture -- Quantum liquid droplets in two component bose mixtures -- Bose einstein condensation of triplons in quantum magnets -- Thermodynamics of dimerized quantum magnets -- Magnetocaloric effect and grueneisen parameter of quantum magnets with spin gap -- Weak anisotropy in spin gapped quantum magnets -- Optimized perturbation theory in δ2 order -- Appendices