Delving further into development, adaptation, and learning, this chapter considers the potential of reward-oriented optimization of behavior. Reinforcement learning (RL) is motivated from the Rescorla–Wagner model in psychology and behaviorism. Next, a detailed introduction to RL in artificial systems is provided. It is shown when and how RL works, but also current shortcomings and challenges are discussed. In conclusion, the chapter emphasizes that behavioral optimization and reward-based behavioral adaptations can be well-accomplished with RL. However, to be able to solve more challenging planning problems and to enable flexible, goal-oriented behavior, hierarchically and modularly structured models about the environment are necessary. Such models then also enable the pursuance of abstract reasoning and of thoughts that are fully detached from the current environmental state. The challenge remains how such models may actually be learned and structured.
Acquisition of Goal-Oriented Behavior for Snake-Like Robot by CPG and Reinforcement Learning
