160 citations · 411 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
KnightCap: A chess program that learns by combining TD(lambda) with game-tree search
Jonathan Baxter, Andrew Tridgell, Lex Weaver
In this paper we present TDLeaf(lambda), a variation on the TD(lambda) algorithm that enables it to be used in conjunction with game-tree search. We present some experiments in whi…
TDLeaf(lambda): Combining Temporal Difference Learning with Game-Tree Search
Jonathan Baxter, Andrew Tridgell, Lex Weaver
In this paper we present TDLeaf(lambda), a variation on the TD(lambda) algorithm that enables it to be used in conjunction with minimax search. We present some experiments in both…