Algorithms for Reinforcement Learning by Csaba Szepesvari

By Csaba Szepesvari

Reinforcement studying is a studying paradigm serious about studying to manage a method in order to maximise a numerical functionality degree that expresses a long term objective.What distinguishes reinforcement studying from supervised studying is that merely partial suggestions is given to the learner in regards to the learner's predictions. extra, the predictions can have long-term results via influencing the longer term country of the managed procedure. therefore, time performs a distinct position. The aim in reinforcement studying is to improve effective studying algorithms, in addition to to appreciate the algorithms' benefits and obstacles. Reinforcement studying is of significant curiosity end result of the huge variety of useful functions that it may be used to handle, starting from difficulties in man made intelligence to operations learn or regulate engineering. during this ebook, we concentrate on these algorithms of reinforcement studying that construct at the robust concept of dynamic programming.We supply a reasonably accomplished catalog of studying difficulties, describe the center principles, observe quite a few state-of-the-art algorithms, by means of the dialogue in their theoretical homes and boundaries.

Show description

Read Online or Download Algorithms for Reinforcement Learning PDF

Similar intelligence & semantics books

Information Modelling and Knowledge Bases XIX

Within the final many years details modelling and information bases became scorching themes not just in educational groups concerning info platforms and laptop technology, but in addition in company components the place details expertise is utilized. This publication contains papers submitted to the seventeenth European-Japanese convention on details Modelling and data Bases (EJC 2007).

Indistinguishability Operators: Modelling Fuzzy Equalities and Fuzzy Equivalence Relations

Indistinguishability operators are crucial instruments in fuzzy good judgment due to the fact that they fuzzify the thoughts of equivalence relation and crisp equality. This booklet collects the entire major features of those operators in one quantity for the 1st time. the strain is wear the examine in their constitution and the monograph starts off featuring the several ways that indistinguishability operators might be generated and represented.

The Turing Test and the Frame Problem: Ai's Mistaken Understanding of Intelligence

Either the Turing try out and the body challenge were major goods of debate because the Nineteen Seventies within the philosophy of man-made intelligence (AI) and the philisophy of brain. notwithstanding, there was little attempt in the course of that point to distill how the body challenge bears at the Turing attempt. If it proves to not be solvable, then not just will the try out now not be handed, however it will name into query the idea of classical AI that intelligence is the manipluation of formal constituens lower than the keep watch over of a application.

Additional resources for Algorithms for Reinforcement Learning

Example text

2. ALGORITHMS FOR LARGE STATE SPACES 31 Algorithm 7 The function implementing the batch-mode λ-LSPE update. This function must be called repeatedly until convergence. function LambdaLSPE(D, θ ) Input: D = ((Xt , At , Rt+1 , Yt+1 ); t = 0, . . , n − 1) is a list of transitions, θ ∈ Rd is the parameter vector 1: A, b, δ ← 0 A ∈ Rd×d , b ∈ Rd , δ ∈ R 2: for t = n − 1 downto 0 do 3: f ← ϕ[Xt ] 4: v←θ f 5: δ ← γ · λ · δ + Rt+1 + γ · θ ϕ[Yt+1 ] − v 6: b ← b + (v + δ) · f 7: A←A+f ·f 8: end for 9: θ ← A−1 b 10: θ ← θ + α · (θ − θ) 11: return θ Thus, in this case, λ-LSPE solves a linear regression problem, implementing the so-called fitted value iteration algorithm for policy evaluation with linear function approximation.

Ik ) (x) = ϕi1 (x1 )ϕi2 (x2 ) . . ϕik (xk ). When X ⊂ Rk , one particularly popular choice is to (1) (d ) use radial basis function (RBF) networks, when ϕ (i) (xi ) = (G(|xi − xi |), . . , G(|xi − xi i |)) . (j ) Here xi ∈ R (j = 1, . . , di ) is fixed by the user and G is a suitable function. A typical choice for G is G(z) = exp(−η z2 ) where η > 0 is a scale parameter. The tensor product construct in this cases places Gaussians at points of a regular grid and the i th basis function becomes ϕi (x) = exp(−η x − x (i) 2 ), where x (i) ∈ X now denotes a point on a regular d1 × .

VALUE PREDICTION PROBLEMS The curse of dimensionality The issue with tensor product constructions, state aggregation and straightforward tile coding is that when the state space is high dimensional they quickly become intractable: For example, a tiling of [0, 1]D with cubical regions with side-lengths of ε gives rise to d = ε −D -dimensional feature- and parameter-vectors. If ε = 1/2 and D = 100, we get the enormous number d ≈ 1030 . This is problematic since state-representations with hundreds of dimensions are common in applications.

Download PDF sample

Rated 4.23 of 5 – based on 33 votes