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By Vedran Kordic

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10. The transition model in the experiments The goal of these experiments is to understand the performance and the accelerated effects. To calculate the steps we assume that the upper left corner is the start state. In the domain transfer cases, we compare the steps of reaching the reward of a random policy, the simple transferred policy, the transferred policy and the optimal one as the performance evaluations. In the task transfer cases, we compare the steps of reaching the reward of a random policy and the transferred policy to evaluate the performance.

Humphrys, M. (1996). Action selection methods using reinforcement learning, From Animals to Animats 4: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, MIT Press, Bradford Books, pp. 135–144. Jouffe, L. (1998). Fuzzy inference system learning by reinforcement methods, IEEE Transactions on Systems, Man and Cybernetics, Part C 28(3): 338–355. Karlsson, J. (1997). Learning to Solve Multiple Goals, PhD thesis, University of Rochester, Rochester, NY, USA. Konidaris, G.

R R (a) source (b) the task transfer R R (c) the topological domain transfer Fig. 2. Examples of transfer types (d) the scaling domain transfer 3. 1 An Example Before we describe how to transfer, we show a simple example for the combinatorial Laplacian and a simple scenario for the transfer problem. A 3x3 grid world and its corresponding state transitioin graph are shown in Fig. 3(a) and Fig. 3(c). A state is defined Graph Laplacian Based Transfer Learning Methods in Reinforcement Learning 33 as an agent at one of cells in the grid world and the state transition graph shows the possible transitions from one state to another.

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