摘要
Reinforcement learning provides a cognitive science perspective to behavior and sequential decision making providedthat reinforcement learning algorithms introduce a computational concept of agency to the learning problem.Hence it addresses an abstract class of problems that can be characterized as follows: An algorithm confronted withinformation from an unknown environment is supposed to find step wise an optimal way to behave based only on somesparse, delayed or noisy feedback from some environment, that changes according to the algorithm’s behavior. Hencereinforcement learning offers an abstraction to the problem of goal-directed learning from interaction. The paper offersan opinionated introduction in the algorithmic advantages and drawbacks of several algorithmic approaches to providealgorithmic design options.
