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ICML
2010
IEEE
13 years 7 months ago
Temporal Difference Bayesian Model Averaging: A Bayesian Perspective on Adapting Lambda
Temporal difference (TD) algorithms are attractive for reinforcement learning due to their ease-of-implementation and use of "bootstrapped" return estimates to make effi...
Carlton Downey, Scott Sanner
CORR
2011
Springer
136views Education» more  CORR 2011»
13 years 1 months ago
Reinforcement Learning for Agents with Many Sensors and Actuators Acting in Categorizable Environments
In this paper, we confront the problem of applying reinforcement learning to agents that perceive the environment through many sensors and that can perform parallel actions using ...
Enric Celaya, Josep M. Porta
IEAAIE
2001
Springer
14 years 2 months ago
On the Relationship between Learning Capability and the Boltzmann-Formula
In this paper a combined use of reinforcement learning and simulated annealing is treated. Most of the simulated annealing methods suggest using heuristic temperature bounds as the...
Péter Stefán, Laszlo Monostori
ECML
2005
Springer
14 years 3 months ago
Towards Finite-Sample Convergence of Direct Reinforcement Learning
Abstract. While direct, model-free reinforcement learning often performs better than model-based approaches in practice, only the latter have yet supported theoretical guarantees f...
Shiau Hong Lim, Gerald DeJong
ATAL
2006
Springer
14 years 1 months ago
Rule value reinforcement learning for cognitive agents
RVRL (Rule Value Reinforcement Learning) is a new algorithm which extends an existing learning framework that models the environment of a situated agent using a probabilistic rule...
Christopher Child, Kostas Stathis