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Planning and Acting in Partially Observable Stochastic Domains
Reinforcement Learning: A Survey
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.
Markov Games as a Framework for Multi-Agent Reinforcement Learning
- M. Littman
- Computer ScienceICML
- 10 July 1994
Measuring praise and criticism: Inference of semantic orientation from association
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
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.
Learning Policies for Partially Observable Environments: Scaling Up
Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach
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
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.
An analysis of model-based Interval Estimation for Markov Decision Processes