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ICONIP
2009
13 years 5 months ago
Tracking in Reinforcement Learning
Reinforcement learning induces non-stationarity at several levels. Adaptation to non-stationary environments is of course a desired feature of a fair RL algorithm. Yet, even if the...
Matthieu Geist, Olivier Pietquin, Gabriel Fricout
CORR
2012
Springer
196views Education» more  CORR 2012»
12 years 3 months ago
PAC-Bayesian Policy Evaluation for Reinforcement Learning
Bayesian priors offer a compact yet general means of incorporating domain knowledge into many learning tasks. The correctness of the Bayesian analysis and inference, however, lar...
Mahdi Milani Fard, Joelle Pineau, Csaba Szepesv&aa...
AGENTS
2001
Springer
14 years 11 days ago
Hierarchical multi-agent reinforcement learning
In this paper, we investigate the use of hierarchical reinforcement learning (HRL) to speed up the acquisition of cooperative multi-agent tasks. We introduce a hierarchical multi-a...
Rajbala Makar, Sridhar Mahadevan, Mohammad Ghavamz...
AGENTS
1999
Springer
14 years 4 days ago
Team-Partitioned, Opaque-Transition Reinforcement Learning
In this paper, we present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the concept of usin...
Peter Stone, Manuela M. Veloso
IJCNN
2006
IEEE
14 years 1 months ago
Reinforcement Learning for Platform-Independent Visual Robot Control
—This paper proposes a new architecture for robot control. A test scenario is outlined to test the proposed system and enable a comparison with an existing system, which is able ...
David Muse, Kevin Burn, Stefan Wermter