114 search results - page 1 / 23 » Temporal Difference Updating without a Learning Rate |
NIPS
2007
13 years 6 months ago
2007
ISDA
2009
IEEE
13 years 11 months ago
2009
IEEE
This paper presents postponed updates, a new strategy for TD methods that can improve sample efficiency without incurring the computational and space requirements of model-based ...
COLT
2000
Springer
13 years 9 months ago
2000
Springer
We give the first rigorous upper bounds on the error of temporal difference (td) algorithms for policy evaluation as a function of the amount of experience. These upper bounds pr...
ML
2002
ACM
13 years 4 months ago
2002
ACM
TD() is a popular family of algorithms for approximate policy evaluation in large MDPs. TD() works by incrementally updating the value function after each observed transition. It h...
ICML
2010
IEEE
13 years 2 months ago
2010
IEEE
Temporal difference (TD) algorithms are attractive for reinforcement learning due to their ease-of-implementation and use of "bootstrapped" return estimates to make effi...