A temporal-difference learning method using gaussian state representation for continuous state space problems

In this paper, we tackle the problem of reinforcement learning (RL) in a continuous state space. An appropriate discretization of the space can make many learning tasks tractable. A method using Gaussian state representation and the Rational Policy Making algorithm (RPM) has been proposed for this problem. This method discretizes the space by constructing a chain of states which represents a path to the goal of the agent exploiting past experiences of reaching it. This method exploits successful experiences strongly. Therefore, it can find a rational solution quickly in an environment with few noises. In a noisy environment, it makes many unnecessary and distractive states and does the task poorly. For learning in such an environment, we have introduced the concept of the value of a state to the above method and developed a new method. This method uses a temporal-difference (TD) learning algorithm for learning the values of states. The value of a state is used to determine the size of the state. Thus, our developed method can trim and eliminate unnecessary and distractive states quickly and learn the task well even in a noisy environment. We show the effectiveness of our method by computer simulations of a path finding task and a cart-pole swing-up task. © The Japanese Society for Artificial Intelligence 2014.