Performance Dynamics and Termination Errors in Reinforcement Learning: A Unifying Perspective

In reinforcement learning, a decision needs to be made at some point as to whether it is worthwhile to carry on with the learning process or to terminate it. In many such situations, stochastic elements are often present which govern the occurrence of rewards, with the sequential occurrences of positive rewards randomly interleaved with negative rewards. For most practical learners, the learning is considered useful if the number of positive rewards always exceeds the negative ones. A situation that often calls for learning termination is when the number of negative rewards exceeds the number of positive rewards. However, while this seems reasonable, the error of premature termination, whereby termination is enacted along with the conclusion of learning failure despite the positive rewards eventually far outnumber the negative ones, can be significant. In this paper, using combinatorial analysis we study the error probability in wrongly terminating a reinforcement learning activity which undermines the effectiveness of an optimal policy, and we show that the resultant error can be quite high. Whilst we demonstrate mathematically that such errors can never be eliminated, we propose some practical mechanisms that can effectively reduce such errors. Simulation experiments have been carried out, the results of which are in close agreement with our theoretical findings.Comment: Short Paper in AIKE 201

Probing Electroweak Symmetry Breaking Mechanism at the LHC: A Guideline from Power Counting Analysis

We formulate the equivalence theorem as a theoretical criterion for sensitively probing the electroweak symmetry breaking mechanism, and develop a precise power counting method for the chiral Lagrangian formulated electroweak theories. Armed with these, we perform a systematic analysis on the sensitivities of the scattering processes $W^\pm W^\pm \rightarrow W^\pm W^\pm$ and $q\bar{q}'\rightarrow W^\pm Z$ for testing all possible effective bosonic operators in the chiral Lagrangian formulated electroweak theories at the CERN Large Hadron Collider (LHC). The analysis shows that these two kinds of processes are "complementary" in probing the electroweak symmetry breaking sector.Comment: Extended version, 11-page-Latex-file and 3 separate PS-Figs. To be Published in Mod.Phys.Lett.

Stochastic Reinforcement Learning

In reinforcement learning episodes, the rewards and punishments are often non-deterministic, and there are invariably stochastic elements governing the underlying situation. Such stochastic elements are often numerous and cannot be known in advance, and they have a tendency to obscure the underlying rewards and punishments patterns. Indeed, if stochastic elements were absent, the same outcome would occur every time and the learning problems involved could be greatly simplified. In addition, in most practical situations, the cost of an observation to receive either a reward or punishment can be significant, and one would wish to arrive at the correct learning conclusion by incurring minimum cost. In this paper, we present a stochastic approach to reinforcement learning which explicitly models the variability present in the learning environment and the cost of observation. Criteria and rules for learning success are quantitatively analyzed, and probabilities of exceeding the observation cost bounds are also obtained.Comment: AIKE 201

Theoretical studies of 63Cu Knight shifts of the normal state of YBa2Cu3O7

The 63Cu Knight shifts and g factors for the normal state of YBa2Cu3O7 in tetragonal phase are theoretically studied in a uniform way from the high (fourth-) order perturbation formulas of these parameters for a 3d9 ion under tetragonally elongated octahedra. The calculations are quantitatively correlated with the local structure of the Cu2+(2) site in YBa2Cu3O7. The theoretical results show good agreement with the observed values, and the improvements are achieved by adopting fewer adjustable parameters as compared to the previous works. It is found that the significant anisotropy of the Knight shifts is mainly attributed to the anisotropy of the g factors due to the orbital interactions.Comment: 5 page

Coulomb Drag in Graphene

We study the Coulomb drag between two single graphene sheets in intrinsic and extrinsic graphene systems with no interlayer tunneling. The general expression for the nonlinear susceptibility appropriate for single-layer graphene systems is derived using the diagrammatic perturbation theory, and the corresponding exact zero-temperature expression is obtained analytically. We find that, despite the existence of a non-zero conductivity in an intrinsic graphene layer, the Coulomb drag between intrinsic graphene layers vanishes at all temperatures. In extrinsic systems, we obtain numerical results and an approximate analytical result for the drag resistivity $\rho_{\textrm{D}}$, and find that $\rho_{\textrm{D}}$ goes as $T^2$ at low temperature $T$, as $1/d^4$ for large bilayer separation $d$ and $1/n^3$ for high carrier density $n$. We also discuss qualitatively the effect of plasmon-induced enhancement on the Coulomb drag, which should occur at a temperature of the order of or higher than the Fermi temperature