1236 search results - page 8 / 248 » Opposition-Based Reinforcement Learning |
209
Voted
ICCCI
2011
Springer
14 years 2 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, ...
126
Voted
NECO
2010
15 years 1 months ago
2010
Most conventional Policy Gradient Reinforcement Learning (PGRL) algorithms neglect (or do not explicitly make use of) a term in the average reward gradient with respect to the pol...
149
Voted
AGI
2011
14 years 6 months ago
2011
We present an actor-critic scheme for reinforcement learning in complex domains. The main contribution is to show that planning and I/O dynamics can be separated such that an intra...
136
Voted
JAIR
2000
15 years 2 months ago
2000
This paper describes a novel method by which a spoken dialogue system can learn to choose an optimal dialogue strategy from its experience interacting with human users. The method...
197
Voted
GECCO
2011
Springer
14 years 6 months ago
2011
Springer
The reward functions that drive reinforcement learning systems are generally derived directly from the descriptions of the problems that the systems are being used to solve. In so...