Strategy selection under entropy measures in movementbased threeway decision Abstract   The trisectingactingoutcome (TAO) model of threeway decisions comprises three components, i.e., constructing a trisection, strategies for action, and measuring the outcome. Existing research seldom involves the third component, i.e., measuring the effectiveness of the outcome. A previous study provides a strategy select method by analyzing benefits or costs in the movementbased threeway decision. However, we often choose a strategy depend on the probability or distribution of three regions instead of the benefits or costs. In this paper, we propose a probabilistic movement model of threeway decision and give a strategy selection mechanism based on information entropy. Conditional entropy and cross entropy are used to select strategies by measuring these probabilities according to the useru0027s preferences and structural features of the three partitions. It selects the action or strategy which has the maximum probability of desirable movements based on the minimization of the conditional entropy. It also selects the action or strategy which has the least difference between actual probabilistic distributions and expected probabilistic distribution based on the minimization of the cross entropy. We give two examples to illustrate the practicality and effectiveness of the strategy selection method in the probabilistic movement model of threeway decision.

Entropic Risk Measure in Policy Search With the increasing pace of automation, modern robotic systems need to act in stochastic, nonstationary, partially observable environments. A range of algorithms for finding parameterized policies that optimize for longterm average performance have been proposed in the past. However, the majority of the proposed approaches does not explicitly take into account the variability of the performance metric, which may lead to finding policies that although performing well on average, can perform spectacularly bad in a particular run or over a period of time. To address this shortcoming, we study an approach to policy optimization that explicitly takes into account higher order statistics of the reward function. In this paper, we extend policy gradient methods to include the entropic risk measure in the objective function and evaluate their performance in simulation experiments and on a realrobot task of learning a hitting motion in robot badminton.