Abstract: Risk quantification, policy search, and automated safe policy deployment techniques are described. In one or more implementations, techniques are utilized to determine safety of a policy, such as to express a level of confidence that a new policy will exhibit an increased measure of performance (e.g., interactions or conversions) over a currently deployed policy. In order to make this determination, reinforcement learning and concentration inequalities are utilized, which generate and bound confidence values regarding the measurement of performance of the policy and thus provide a statistical guarantee of this performance. These techniques are usable to quantify risk in deployment of a policy, select a policy for deployment based on estimated performance and a confidence level in this estimate (e.g.

Abstract: Risk quantification, policy search, and automated safe policy deployment techniques are described. In one or more implementations, techniques are utilized to determine safety of a policy, such as to express a level of confidence that a new policy will exhibit an increased measure of performance (e.g., interactions or conversions) over a currently deployed policy. In order to make this determination, reinforcement learning and concentration inequalities are utilized, which generate and bound confidence values regarding the measurement of performance of the policy and thus provide a statistical guarantee of this performance. These techniques are usable to quantify risk in deployment of a policy, select a policy for deployment based on estimated performance and a confidence level in this estimate (e.g.

Abstract: Risk quantification, policy search, and automated safe policy deployment techniques are described. In one or more implementations, techniques are utilized to determine safety of a policy, such as to express a level of confidence that a new policy will exhibit an increased measure of performance (e.g., interactions or conversions) over a currently deployed policy. In order to make this determination, reinforcement learning and concentration inequalities are utilized, which generate and bound confidence values regarding the measurement of performance of the policy and thus provide a statistical guarantee of this performance. These techniques are usable to quantify risk in deployment of a policy, select a policy for deployment based on estimated performance and a confidence level in this estimate (e.g.

Abstract: An annular friction liner for a bridging clutch of a torque converter. The liner has a radially extending friction surface engageable with a reaction surface of the torque converter. The friction surface has a plurality of circumferentially spaced grooves extending from a radially inner edge of the friction surface to a radially outer edge thereof. Each groove extends along a serpentine path and has a series of contiguous groove sections which interconnect end-to-end at a substantial angle to one another. This groove path provides a mixing module for promoting turbulence.