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» Feudal Reinforcement Learning
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AI
2002
Springer
13 years 8 months ago
Multiagent learning using a variable learning rate
Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on ...
Michael H. Bowling, Manuela M. Veloso
AAMAS
2007
Springer
13 years 8 months ago
Parallel Reinforcement Learning with Linear Function Approximation
In this paper, we investigate the use of parallelization in reinforcement learning (RL), with the goal of learning optimal policies for single-agent RL problems more quickly by us...
Matthew Grounds, Daniel Kudenko
NIPS
2008
13 years 9 months ago
Policy Search for Motor Primitives in Robotics
Many motor skills in humanoid robotics can be learned using parametrized motor primitives as done in imitation learning. However, most interesting motor learning problems are high...
Jens Kober, Jan Peters
AAAI
2006
13 years 9 months ago
Using Homomorphisms to Transfer Options across Continuous Reinforcement Learning Domains
We examine the problem of Transfer in Reinforcement Learning and present a method to utilize knowledge acquired in one Markov Decision Process (MDP) to bootstrap learning in a mor...
Vishal Soni, Satinder P. Singh
ATAL
2006
Springer
13 years 11 months ago
Probabilistic policy reuse in a reinforcement learning agent
We contribute Policy Reuse as a technique to improve a reinforcement learning agent with guidance from past learned similar policies. Our method relies on using the past policies ...
Fernando Fernández, Manuela M. Veloso