85 search results - page 15 / 17 » Solving Stochastic Planning Problems with Large State and Ac... |
KER
2007
13 years 7 months ago
2007
AI planning solves the problem of generating a correct and efficient ordered set of instantiated activities, from a knowledge base of generic actions, which when executed will tra...
NIPS
2003
13 years 8 months ago
2003
We study an approach to policy selection for large relational Markov Decision Processes (MDPs). We consider a variant of approximate policy iteration (API) that replaces the usual...
AAAI
2006
13 years 8 months ago
2006
DTGolog, a decision-theoretic agent programming language based on the situation calculus, was proposed to ease some of the computational difficulties associated with Markov Decisi...
ICML
2003
IEEE
14 years 8 months ago
2003
IEEE
Hierarchical reinforcement learning is a general framework which attempts to accelerate policy learning in large domains. On the other hand, policy gradient reinforcement learning...
ATAL
2010
Springer
13 years 8 months ago
2010
Springer
Memory-bounded techniques have shown great promise in solving complex multi-agent planning problems modeled as DEC-POMDPs. Much of the performance gains can be attributed to pruni...