160 citations · 411 across the 10 of their papers we have counts for
10 papers
Reinforcement Learning From State and Temporal Differences
Lex Weaver, Jonathan Baxter
TD() with function approximation has proved empirically successful for some complex reinforcement learning problems. For linear approximation, TD() has been shown to minimise…
A Multi-Agent, Policy-Gradient approach to Network Routing
Nigel Tao, Jonathan Baxter, Lex Weaver
Network routing is a distributed decision problem which naturally admits numerical performance measures, such as the average time for a packet to travel from source to destination.…
The Optimal Reward Baseline for Gradient-Based Reinforcement Learning
Lex Weaver, Nigel Tao
There exist a number of reinforcement learning algorithms which learnby climbing the gradient of expected reward. Their long-runconvergence has been proved, even in partially obser…
Experiments with Infinite-Horizon, Policy-Gradient Estimation
J. Baxter, P. L. Bartlett, L. Weaver
In this paper, we present algorithms that perform gradient ascent of the average reward in a partially observable Markov decision process (POMDP). These algorithms are based on GPO…
Sorting Integers on the AP1000
Lex Weaver, Andrew Lynes
Sorting is one of the classic problems of computer science. Whilst well understood on sequential machines, the diversity of architectures amongst parallel systems means that algori…
Design and Evaluation of Mechanisms for a Multicomputer Object Store
Lex Weaver
Multicomputers have traditionally been viewed as powerful compute engines. It is from this perspective that they have been applied to various problems in order to achieve significa…