Reinforcement learning and its application to control
Vijaykumar Gullapalli · 1992
Learning control involves modifying a controller's behavior to improve its performance as measured by some predefined index of performance (IP). If control actions that improve performance as measured by the IP are known, supervised learning methods, or methods for learning from examples, can be used to train the controller. But when such control actions are not known a priori, appropriate control behavior has to be inferred from observations of the IP. One can distinguish between two classes of methods for training controllers under such circumstances. Indirect methods involve constructing a model of the problem's IP and using the model to obtain training information for the controller. On the other hand, direct, or model-free, methods obtain the requisite training information by observing the effects of perturbing the controlled process on the IP. Despite its reputation for inefficiency, we argue that for certain types of problems the latter approach, of which reinforcement learning is an example, can yield faster, more reliable learning. Using several control problems as examples, we illustrate how the complexity of model construction can often exceed that of solving the original control problem using direct reinforcement learning methods, making indirect methods relatively inefficient. These results indicate the importance of considering direct reinforcement learning methods as tools for learning to solve control problems. We also present several techniques for augmenting the power of reinforcement learning methods. These include (1) the use of local models to guide assigning credit to the components of a reinforcement learning system, (2) implementing a procedure from experimental psychology called "shaping" to improve the efficiency of learning, thereby making more complex problems amenable to solution, and (3) implementing a multi-level learning architecture designed for exploiting task decomposability by using previously-learned behaviors as primitives for learning more complex tasks.