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.

Reinforcement Learning of Trajectory Distributions: Applications in Assisted Teleoperation and Motion Planning The majority of learning from demonstration approaches do not address suboptimal demonstrations or cases when drastic changes in the environment occur after the demonstrations were made. For example, in real teleoperation tasks, the demonstrations provided by the user are often suboptimal due to interface and hardware limitations. In tasks involving comanipulation and manipulation planning, the environment often changes due to unexpected obstacles rendering previous demonstrations invalid. This paper presents a reinforcement learning algorithm that exploits the use of relevance functions to tackle such problems. This paper introduces the Pearson correlation as a measure of the relevance of policy parameters in regards to each of the components of the cost function to be optimized. The method is demonstrated in a static environment where the quality of the teleoperation is compromised by the visual interface (operating a robot in a threedimensional task by using a simple 2D monitor). Afterward, we tested the method on a dynamic environment using a real 7DoF robot arm where distributions are computed online via Gaussian Process regression.