Planning and acting in partially observable stochastic domains
Artificial Intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
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A common solution approach to reinforcement learning problems with large state spaces (where value functions cannot be represented exactly) is to compute an approximation of the value function in terms of state features. However, little attention has been paid to the cost of computing these state features (e.g., search-based features). To this end, we introduce a cost-sensitive sparse linear-value function approximation algorithm ---FOVEA ---and demonstrate its performance on an experimental domain with a range of feature costs.