%0 Conference Paper %1 antos2007 %A Antos, A. %A Munos, R. %A Szepesvári, Cs. %B NIPS %D 2007 %K actor-critic approximation, batch bounds, function learning, methods, nonparametrics performance reinforcement %P 9--16 %T Fitted Q-iteration in Continuous Action-space MDPs %X We consider continuous state, continuous action batch reinforcement learning where the goal is to learn a good policy from a sufficiently rich trajectory generated by some policy. We study a variant of fitted Q-iteration, where the greedy action selection is replaced by searching for a policy in a restricted set of candidate policies by maximizing the average action values. We provide a rigorous analysis of this algorithm, proving what we believe is the first finite-time bound for value-function based algorithms for continuous state and action problems. Note: In retrospect, it would have been better to call this algorithm an actor-critic algorithm. The algorithm that we considers updates a policy and a value function (action-value function in this case).

@inproceedings{antos2007, abstract = {We consider continuous state, continuous action batch reinforcement learning where the goal is to learn a good policy from a sufficiently rich trajectory generated by some policy. We study a variant of fitted Q-iteration, where the greedy action selection is replaced by searching for a policy in a restricted set of candidate policies by maximizing the average action values. We provide a rigorous analysis of this algorithm, proving what we believe is the first finite-time bound for value-function based algorithms for continuous state and action problems. Note: In retrospect, it would have been better to call this algorithm an actor-critic algorithm. The algorithm that we considers updates a policy and a value function (action-value function in this case).}, added-at = {2020-03-17T03:03:01.000+0100}, author = {Antos, A. and Munos, R. and Szepesv{\'a}ri, {Cs}.}, biburl = {https://www.bibsonomy.org/bibtex/215e32a7a420729a8e0e3f367d50df57c/csaba}, booktitle = {NIPS}, crossref = {NIPS20}, date-added = {2010-08-28 17:38:14 -0600}, date-modified = {2010-11-25 00:50:53 -0700}, interhash = {d72a3b55d770d0096a2489e139a74a52}, intrahash = {15e32a7a420729a8e0e3f367d50df57c}, keywords = {actor-critic approximation, batch bounds, function learning, methods, nonparametrics performance reinforcement}, pages = {9--16}, pdf = {papers/rlca.pdf}, timestamp = {2020-03-17T03:03:01.000+0100}, title = {Fitted {Q}-iteration in Continuous Action-space {MDP}s}, year = 2007 }