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Extended Q-Learning
Presented by: Pat Leang
ABSTRACT: Reinforcement learning methods are based on the
mapping of state space to an action space. The mapping is
done via sensing of environmental input. Errors are then the
result of state space sampling. An approach called Extended
Q-learning (EQ-learning) is implemented to overcome this error.
In this approach, weighted base functions are formed which
approximates a continuious value, appropriate for the continuous
sensor values obtained from the autonomous robot. The approach
is based on stochastic field model and promises to improve
learning performance over ordinary learning methods.
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