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» Using inaccurate models in reinforcement learning
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ICML
2006
IEEE
15 years 10 months ago
Using inaccurate models in reinforcement learning
In the model-based policy search approach to reinforcement learning (RL), policies are found using a model (or "simulator") of the Markov decision process. However, for ...
Pieter Abbeel, Morgan Quigley, Andrew Y. Ng
ATAL
2009
Springer
15 years 4 months ago
Generalized model learning for reinforcement learning in factored domains
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...
Todd Hester, Peter Stone
IJCAI
2001
14 years 11 months ago
R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning
R-max is a very simple model-based reinforcement learning algorithm which can attain near-optimal average reward in polynomial time. In R-max, the agent always maintains a complet...
Ronen I. Brafman, Moshe Tennenholtz
CORR
1998
Springer
164views Education» more  CORR 1998»
14 years 9 months ago
Training Reinforcement Neurocontrollers Using the Polytope Algorithm
A new training algorithm is presented for delayed reinforcement learning problems that does not assume the existence of a critic model and employs the polytope optimization algorit...
Aristidis Likas, Isaac E. Lagaris
SAC
2005
ACM
15 years 3 months ago
Reinforcement learning agents with primary knowledge designed by analytic hierarchy process
This paper presents a novel model of reinforcement learning agents. A feature of our learning agent model is to integrate analytic hierarchy process (AHP) into a standard reinforc...
Kengo Katayama, Takahiro Koshiishi, Hiroyuki Narih...