MICAI
2009
Springer
14 years 7 months ago
2009
Springer
Reinforcement Learning is a commonly used technique in robotics, however, traditional algorithms are unable to handle large amounts of data coming from the robot’s sensors, requi...
ICCBR
2009
Springer
14 years 7 months ago
2009
Springer
This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Reinforcement Learning algorithms, combining Case Based Reasoning (CBR) and ...
GECCO
2009
Springer
14 years 7 months ago
2009
Springer
Helicopter hovering is an important challenge problem in the field of reinforcement learning. This paper considers several neuroevolutionary approaches to discovering robust cont...
ATAL
2009
Springer
14 years 7 months ago
2009
Springer
Creating coordinated multiagent policies in environments with uncertainty is a challenging problem, which can be greatly simplified if the coordination needs are known to be limi...
ATAL
2009
Springer
14 years 7 months ago
2009
Springer
Improving the sample efficiency of reinforcement learning algorithms to scale up to larger and more realistic domains is a current research challenge in machine learning. Model-ba...
ATAL
2009
Springer
14 years 7 months ago
2009
Springer
In several agent-oriented scenarios in the real world, an autonomous agent that is situated in an unknown environment must learn through a process of trial and error to take actio...
ATAL
2009
Springer
14 years 7 months ago
2009
Springer
We consider the setting of multiple collaborative agents trying to complete a set of tasks as assigned by a centralized controller. We propose a scalable method called“Assignmen...
ATAL
2009
Springer
14 years 7 months ago
2009
Springer
We present a general methodology to automate the search for equilibrium strategies in games derived from computational experimentation. Our approach interleaves empirical game-the...
ATAL
2009
Springer
14 years 7 months ago
2009
Springer
Reinforcement learning algorithms that use eligibility traces, such as Sarsa(λ), have been empirically shown to be effective in learning good estimated-state-based policies in pa...
ATAL
2009
Springer
14 years 7 months ago
2009
Springer
One of the key problems in reinforcement learning is balancing exploration and exploitation. Another is learning and acting in large or even continuous Markov decision processes (...