We propose a new approach to solving dynamic decision problems with unbounded rewards , based on an application of the Q-learning transform to the Bellman equation. Q-learning is a technique from the reinforcement learning literature with strong convergence properties. In our case, the objective of the transform is to convert an unbounded dynamic program into a bounded one. The approach is general enough to handle problems for which existing methods struggle, and yet is accessible to applied researchers and Ph.D. students . We show by example that many common decision problems satisfy our conditions.
We propose a new approach to solving dynamic decision problems with unbounded rewards based on the transformations used in Q-learning . In our case, the objective of the transform is to convert an unbounded dynamic program into a bounded one. The approach is general enough to handle problems for which existing methods struggle, and yet simple relative to other techniques and accessible for applied work . We show by example that many common decision problems satisfy our conditions.
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transform to the Bellman equation. Q-learning is a technique from the reinforcement learning literature with strong convergence properties.
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is accessible to applied researchers and Ph.D. students
simple relative to other techniques and accessible for applied work