| Research Abstract |
Recent research on reinforcement learning (RL) algorithms has concentrated on partially observable Markov decision problems (POMDPs). A possible solution to POMDPs is to use history information to estimate state. Q values must be updated in the form reflecting past history of observation/action pairs. In this study, we developed two methods of reinforcement learning, which can solve certain types of POMDPs. The results are summarized as follows :
(1) As a result of last Grant-in-Aid for Scientific Research (C)(2), we proposed Labeling Q-learning (LQ-learning), which has a new memory architecture of handling past history. In this study, we established a general framework of the LQ-learning. Various algorithms in this framework were devised, and we gave comparative study between these through simulation. The above LQ-learning, however, has the drawback that we must predefine the labeling mechanism. To overcome this drawback, we further devised a SOM (self-organizing feature map) approach of labeling, in which past history of observation/action pairs are partitioned into classes. The SOM has one-dimensional structure and the output nodes of the SOM produce labels.
(2) We proposed a new type of hierarchical RL, called Switching Q-learning (SQ-learning). The basic idea of SQ-learning is that non-Markovian tasks can be automatically decomposed into subtasks solvable by memoryless policies, without any other information leading to "good" subgoals. To deal with such decomposition, SQ-learning employs ordered sequences of Q-modules in which each module discovers a local control policy. SQ-learning uses a hierarchical system of learning automata for switching module. The simulation results demonstrate that SQ-learning has the ability to quickly learn optimal or near-optimal policies without huge computational burden.
It is a future work to build a unified view by which LQ-learning and SQ-learning can be dealt with systematically.
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