23 search results - page 2 / 5 » Combining Stochastic Task Models with Reinforcement Learning... |
ROBOCUP
2007
Springer
13 years 11 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...
NIPS
1996
13 years 6 months ago
1996
Model learning combined with dynamic programming has been shown to be e ective for learning control of continuous state dynamic systems. The simplest method assumes the learned mod...
ATAL
2007
Springer
13 years 11 months ago
2007
Springer
Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...
ECML
2004
Springer
13 years 10 months ago
2004
Springer
In this paper, we show how the dynamics of Q-learning can be visualized and analyzed from a perspective of Evolutionary Dynamics (ED). More specifically, we show how ED can be use...
ATAL
2009
Springer
13 years 12 months ago
2009
Springer
This paper introduces a new model, i.e. state-coupled replicator dynamics, expanding the link between evolutionary game theory and multiagent reinforcement learning to multistate ...