Learning to Solve Multiple Goals
Jonas Karlsson · 1997
In many domains, the task can be decomposed into a set of independent subgoals. Often, such tasks are too complex to be learned using standard techniques such as Reinforcement Learning. The complexity is caused by the learning system having to keep track of the status of all sub-goals concurrently. Thus, if the solution to one sub-goal is known when another sub-goal is in some given state, the known solution must be relearned when the status of the other sub-goal changes. This dissertation presents a modular approach to reinforcement learning that takes advantage of task decomposition to avoid unnecessary relearning. In the modular approach, modules are created to learn each sub-goal. Each module receives only those inputs relevant to its associated sub-goal, and can therefore learn without being affected by the state of other sub-goals. Furthermore, each module searches a much smaller space than that defined by all inputs considered together, thereby greatly reducing learning time. Si...