458 search results - page 17 / 92 » Q-Decomposition for Reinforcement Learning Agents |
ATAL
2008
Springer
13 years 9 months ago
2008
Springer
This paper presents the dynamics of multi-agent reinforcement learning in multiple state problems. We extend previous work that formally modelled the relation between reinforcemen...
IJCAI
2007
13 years 9 months ago
2007
Deictic representation is a representational paradigm, based on selective attention and pointers, that allows an agent to learn and reason about rich complex environments. In this...
ICCCI
2011
Springer
12 years 7 months ago
2011
Springer
Multiagent reinforcement learning problems are especially difficult because of their dynamism and the size of joint state space. In this paper a new benchmark problem is proposed, ...
AIIDE
2006
13 years 9 months ago
2006
Reinforcement learning is an effective machine learning paradigm in domains represented by compact and discrete state-action spaces. In high-dimensional and continuous domains, ti...
ATAL
2006
Springer
13 years 11 months ago
2006
Springer
We contribute Policy Reuse as a technique to improve a reinforcement learning agent with guidance from past learned similar policies. Our method relies on using the past policies ...