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Multi-objective Reinforcement Learning
In this talk we present PQ-learning, a new Reinforcement Learning (RL) algorithm that
determines the rational behaviours of an agent in multi-objective domainsThis work is partially funded by: grant TIN2009-14179 (Spanish Government, Plan Nacional de I+D+i)
and Universidad de Málaga, Campus de Excelencia Internacional AndalucĂa Tech. Manuela Ruiz-Montiel
is funded by the Spanish Ministry of Education through the National F.P.U. Progra
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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 representation. The algorithm attaches values to learned rules by adapting reinforcement learning. Structure captured by the rules is used to form a policy. The resulting rule values represent the utility of taking an action if the rule`s conditions are present in the agent`s current percept. Advantages of the new framework are demonstrated, through examples in a predator-prey environment
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