181 search results - page 7 / 37 » On Policy Learning in Restricted Policy Spaces |
ATAL
2004
Springer
14 years 1 months ago
2004
Springer
This paper investigates a relatively new direction in Multiagent Reinforcement Learning. Most multiagent learning techniques focus on Nash equilibria as elements of both the learn...
ICML
2003
IEEE
14 years 8 months ago
2003
IEEE
We present a learning framework for Markovian decision processes that is based on optimization in the policy space. Instead of using relatively slow gradient-based optimization al...
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, ...
ICANNGA
2007
Springer
14 years 1 months ago
2007
Springer
Reinforcement Learning (RL) is analyzed here as a tool for control system optimization. State and action spaces are assumed to be continuous. Time is assumed to be discrete, yet th...
FLAIRS
2004
13 years 9 months ago
2004
er provides new techniques for abstracting the state space of a Markov Decision Process (MDP). These techniques extend one of the recent minimization models, known as -reduction, ...