Developing focus of attention strategies using reinforcement learning

Robot and AI agents that can adapt and handle multiple tasks are the need of today. This requires them to have the capability to handle real world situations. Robots use sensors to interact with the world. Processing the raw data from these sensors becomes computationally intractable in real time. This problem can be addressed by learning strategies of focus of attention. This thesis presents an approach that considers focus of attention as a reinforcement learning problem of selecting controller and feature pairs to be processed at any given point in time. The result is a sensing and control policy that is task specific and can adapt to real world situations using the feedback from the world.; Handling all the information of the world for the successful completion of a task is computationally intractable. In order to resolve this, the current approach is further augmented with short term memory. This enables the agent to learn a memory policy. The memory policy tells the agent what to remember and when to remember in order to successfully complete a task. The approach is illustrated using a number of tasks in the blocks world domain.