Almost Sure Convergence of Average Reward Temporal Difference ...

We are the first to prove that, under very mild conditions, tabular average reward TD converges almost surely to a sample path dependent fixed point.

After at least 25 years since its discovery, we are finally able to provide a long-awaited almost sure convergence analysis. Namely, we are the ...

Almost Sure Convergence of Average Reward Temporal Difference ...

Namely, we are the first to prove that, under very mild conditions, tabular average reward TD con- verges almost surely to a sample path ...

(PDF) Almost Sure Convergence of Average Reward Temporal ...

PDF | Tabular average reward Temporal Difference (TD) learning is perhaps the simplest and the most fundamental policy evaluation algorithm in average.

Almost Sure Convergence of Average Reward Temporal Difference ...

The text discusses the almost sure convergence analysis of Tabular Average Reward Temporal Difference (TD) learning, a fundamental policy evaluation ...

Almost Sure Convergence of Average Reward Temporal Difference ...

The research paper investigates the almost sure convergence of average reward temporal difference (ARTD) learning, a reinforcement learning ...

Almost Sure Convergence of Average Reward Temporal Difference ...

Tabular average reward Temporal Difference (TD) learning is perhaps the simplest and the most fundamental policy evaluation algorithm in average reward ...

Almost Sure Convergence of Linear Temporal Difference Learning ...

Under these assumptions, the authors prove that the linear TD learning algorithm converges almost surely to the unique fixed point of the ...

Convergent Temporal-Difference Learning with Arbitrary Smooth ...

We present a Bellman error objective function and two gradient-descent TD algorithms that optimize it. We prove the asymptotic almost-sure convergence of both ...

Almost Sure Convergence of Linear Temporal Difference Learning ...

This work is the first to establish the almost sure convergence of linear TD without requiring linearly independent features. In fact, we do not ...

Convergence Results for Some Temporal Difference Methods ... - MIT

We also show that the convergence rate of both the discounted and the average cost methods is optimal within the class of temporal differ- ence methods.

On Convergence of Emphatic Temporal-Difference Learning

Remark 4.1 (Almost sure convergence of regular off-policy TD(λ)) If λ is a constant sufficiently close to 1, the matrix associated with the “mean updates” of ...

On Convergence of Average-Reward Off-Policy Control Algorithms ...

a step-size sequence, and δt, the temporal-difference (TD) error, is: ... Theorem 6 Under Assumptions 1-11, General RVI Q ((39)) converges, almost surely, Qn to Q ...

Temporal-difference learning with nonlinear function approximation

We then compare such lazy models with their mean-field counterpart in terms of accuracy and convergence for TD ... 2.2, 2.4) guaranteeing almost sure convergence ...

Neural Temporal-Difference Learning Converges to Global Optima

ment to learn the optimal policy that maximizes the expected total reward. ... almost surely that. Eµ⇥1{|w>x|  т} w⇤  c0 · т/kwk2. (4.9). Assumption ...

An Analysis of Quantile Temporal-Difference Learning

are required in typical proofs of convergence for classical TD ... converges almost surely to the set of fixed points of the projected distributional.

On Convergence of some Gradient-based Temporal-Differences ...

This work considers off-policy temporal-difference (TD) learning methods for policy evaluation in Markov decision processes with finite spaces and ...

A Finite Time Analysis of Temporal Difference Learning with Linear ...

the almost-sure convergence of stochastic approxi- mation algorithms to the invariant set of a certain mean differential equation. The technique greatly.

Gradient Temporal Difference with Momentum: Stability and ...

mentum parameter that ensures almost sure convergence of these algorithms ... It predicts the average accumulated reward an agent would receive from a.

Weak Convergence Properties of Constrained Emphatic Temporal ...

a random reward with mean r(s, a, s0) and bounded variance, according to ... converges to. ¯ h(θ) in mean and almost surely. 3. Convergence Results for ...