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Reinforcement Learning: A Survey
TLDR
Central issues of reinforcement learning are discussed, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state.
Measuring praise and criticism: Inference of semantic orientation from association
TLDR
This article introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words, based on two different statistical measures of word association.
Activity Recognition from Accelerometer Data
TLDR
This paper reports on the efforts to recognize user activity from accelerometer data and performance of base-level and meta-level classifiers, and Plurality Voting is found to perform consistently well across different settings.
Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach
TLDR
In simple experiments involving a 36-node, irregularly connected network, Q-routing proves superior to a nonadaptive algorithm based on precomputed shortest paths and is able to route efficiently even when critical aspects of the simulation, such as the network load, are allowed to vary dynamically.
Acting Optimally in Partially Observable Stochastic Domains
TLDR
The existing algorithms for computing optimal control strategies for partially observable stochastic environments are found to be highly computationally inefficient and a new algorithm is developed that is empirically more efficient.
Friend-or-Foe Q-learning in General-Sum Games
This paper describes an approach to reinforcement learning in multiagent general-sum games in which a learner is told to treat each other agent as either a \friend" or \foe". This Q-learning-style
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