171 search results - page 7 / 35 » Principled Methods for Advising Reinforcement Learning Agent... |
ATAL
2008
Springer
13 years 9 months ago
2008
Springer
Many multiagent problems comprise subtasks which can be considered as reinforcement learning (RL) problems. In addition to classical temporal difference methods, evolutionary algo...
ICML
2006
IEEE
14 years 8 months ago
2006
IEEE
How should a reinforcement learning agent act if its sole purpose is to efficiently learn an optimal policy for later use? In other words, how should it explore, to be able to exp...
PRICAI
2000
Springer
13 years 11 months ago
2000
Springer
When we construct an agent by integrating modules, there appear troubles concerning the autonomy of the agent if we introduce a heuristics that dominates the whole agent. Thus, we ...
ROBOCUP
2007
Springer
14 years 1 months ago
2007
Springer
Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the en...
AAMAS
2007
Springer
13 years 7 months ago
2007
Springer
In this paper, we investigate the use of parallelization in reinforcement learning (RL), with the goal of learning optimal policies for single-agent RL problems more quickly by us...