This text explains reinforcement learning,
a machine learning technique inspired by operant conditioning. It uses
the example of a robotic dog learning to kick a soccer ball to illustrate the core concepts, such as rewards, states, actions, and Q-tables. The text discusses challenges in applying reinforcement learning to real-world scenarios, including the limitations of Q-tables for complex environments and the difficulties of real-world training. Simulations are presented as a solution
to these problems, although limitations in transferring simulated
learning to the real world are acknowledged. The text concludes by
noting that the most successful applications of reinforcement learning
to date have been in the domain of game playing.
