121 search results - page 2 / 25 » Investigating practical, linear temporal difference learning |
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...
ML
2002
ACM
13 years 4 months ago
2002
ACM
We provide an analytical comparison between discounted and average reward temporal-difference (TD) learning with linearly parameterized approximations. We first consider the asympt...
ICML
2009
IEEE
14 years 5 months ago
2009
IEEE
We consider the task of reinforcement learning with linear value function approximation. Temporal difference algorithms, and in particular the Least-Squares Temporal Difference (L...
ML
2000
ACM
13 years 4 months ago
2000
ACM
In this paper we present TDLEAF( ), a variation on the TD( ) algorithm that enables it to be used in conjunction with game-tree search. We present some experiments in which our che...
ICML
2010
IEEE
13 years 6 months ago
2010
IEEE
We consider approximate policy evaluation for finite state and action Markov decision processes (MDP) in the off-policy learning context and with the simulation-based least square...