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ECML
2006
Springer
15 years 10 months ago
Scaling Model-Based Average-Reward Reinforcement Learning for Product Delivery
Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity. We present approaches that ...
Scott Proper, Prasad Tadepalli
WSC
2007
15 years 8 months ago
Optimizing time warp simulation with reinforcement learning techniques
Adaptive Time Warp protocols in the literature are usually based on a pre-defined analytic model of the system, expressed as a closed form function that maps system state to cont...
Jun Wang, Carl Tropper
HEURISTICS
2008
170views more  HEURISTICS 2008»
15 years 6 months ago
Accelerating autonomous learning by using heuristic selection of actions
This paper investigates how to make improved action selection for online policy learning in robotic scenarios using reinforcement learning (RL) algorithms. Since finding control po...
Reinaldo A. C. Bianchi, Carlos H. C. Ribeiro, Anna...
ICML
2003
IEEE
16 years 7 months ago
Q-Decomposition for Reinforcement Learning Agents
The paper explores a very simple agent design method called Q-decomposition, wherein a complex agent is built from simpler subagents. Each subagent has its own reward function and...
Stuart J. Russell, Andrew Zimdars
CLIMA
2004
15 years 7 months ago
Learning in BDI Multi-agent Systems
Abstract. This paper deals with the issue of learning in multi-agent systems (MAS). Particularly, we are interested in BDI (Belief, Desire, Intention) agents. Despite the relevance...
Alejandro Guerra-Hernández, Amal El Fallah-...