Reinforcement Learning Approach to Nonequilibrium Quantum Thermodynamics

Reinforcement learning: An agent observes the environment acquiring its state (straight arrow), then decides to implement an action (upper curved arrow) thus updating the environment state for the next step. Based on the outcomes an interpreter grants the agent a reward R (lower curved arrow), which the agent aims to maximize.