Download Adaptive Representations for Reinforcement Learning by Shimon Whiteson PDF

By Shimon Whiteson

This publication provides new algorithms for reinforcement studying, a kind of desktop studying during which an independent agent seeks a keep watch over coverage for a sequential determination activity. due to the fact that present equipment more often than not depend on manually designed answer representations, brokers that instantly adapt their very own representations have the capability to dramatically increase functionality. This e-book introduces novel ways for instantly researching high-performing representations. the 1st method synthesizes temporal distinction tools, the normal method of reinforcement studying, with evolutionary tools, which could study representations for a large type of optimization difficulties. This synthesis is finished by means of customizing evolutionary the right way to the online nature of reinforcement studying and utilizing them to conform representations for price functionality approximators. the second one procedure immediately learns representations according to piecewise-constant approximations of worth services. It starts off with coarse representations and progressively refines them in the course of studying, interpreting the present coverage and price functionality to infer the simplest refinements. This booklet additionally introduces a singular approach for devising enter representations. this system addresses the characteristic choice challenge via extending an set of rules that evolves the topology and weights of neural networks such that it evolves their inputs too. as well as introducing those new equipment, this publication provides huge empirical leads to a number of domain names demonstrating that those options can considerably enhance functionality over tools with guide representations.

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In the first stage, the learning performed by individuals during their lifetimes speeds evolution, because each individual does not have to be exactly right at birth; it need only be in the right neighborhood and learning can adjust it accordingly. In the second stage, those behaviors that were previously learned during individuals’ lifetimes become known at birth. This stage occurs because individuals that possess adaptive behaviors at birth have higher overall fitness and are favored by evolution.

E. the generation champions, perform substantially higher than this average. However, using their performance as an evaluation metric would ignore the on-line cost that was incurred by evaluating the rest of population and receiving less reward per episode. 5 plots, for the same experiments, the total cumulative reward accrued by each method over the entire run. In both graphs, error bars indicate 95% confidence intervals and Student’s t-tests confirm, with 95% confidence, the statistical significance of the performance difference between each pair of methods except between softmax and interval estimation.

We plot average reward because it is an on-line metric: it measures the amount of reward the agent accrues while it is learning. e. the generation champions, perform substantially higher than this average. However, using their performance as an evaluation metric would ignore the on-line cost that was incurred by evaluating the rest of population and receiving less reward per episode. 5 plots, for the same experiments, the total cumulative reward accrued by each method over the entire run. In both graphs, error bars indicate 95% confidence intervals and Student’s t-tests confirm, with 95% confidence, the statistical significance of the performance difference between each pair of methods except between softmax and interval estimation.

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