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Classification-based Approximate Policy Iteration: Experiments and Extended Discussions
[article]
2014
arXiv
pre-print
Tackling large approximate dynamic programming or reinforcement learning problems requires methods that can exploit regularities, or intrinsic structure, of the problem in hand. Most current methods are geared towards exploiting the regularities of either the value function or the policy. We introduce a general classification-based approximate policy iteration (CAPI) framework, which encompasses a large class of algorithms that can exploit regularities of both the value function and the policy
arXiv:1407.0449v1
fatcat:lsnn4di2ongvbpk34v6nxkgsly