Fitted Q-iteration in continuous action-space MDPs Andras´ Antos Computer and Automation Research Inst. of the Hungarian Academy of Sciences Kende u. 13-17, Budapest 1111, Hungary antos@sztaki.hu Remi Munos´ SequeL project-team, INRIA Lille 59650 Villeneuve d’Ascq, France remi.munos@inria.fr Csaba Szepesv´ari ? Department of Computing …
Fitted Q-iteration can be v iewed as approximate value iteration applied to action-value func- tions. T o see this note that value iteration would assign the value ( T Q k )( x, a ) = r ( x, a ) +, 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 …
Fitted Q-iteration in continuous action-space MDPs . Download. Fitted Q-iteration in continuous action-space MDPs . Csaba Szepesvari. P (·|X t , A t ),where A t is sampled from the distribution determined by ?. We use Q ? : X × A ? R to denote the action-value function of policy ?:Q ? (x, a) = E ? [ ? t=0 ? t R t |X 0 = x, A 0 = a …
Fitted Q-iteration in continuous action-space MDPs Andras Antos´ Computer and Automation Research Inst. of the Hungarian Academy of Sciences Kende u. 13-17, Budapest 1111, Hungary antos@sztaki.hu Remi Munos´ SequeL project-team, INRIA Lille 59650 Villeneuve d’Ascq, France remi.munos@inria.fr Csaba Szepesv´ari ? Department of Computing …
Fitted Q-iteration in continuous action-space MDPs Andr as Antos´ Computer and Automation Research Inst. of the Hungarian Academy of Sciences Kende u. 13-17, Budapest 1111, Hungary antos@sztaki.hu R emi Munos´ SequeL project-team, INRIA Lille 59650 Villeneuve d’Ascq, France remi.munos@inria.fr Csaba Szepesv ´ari ¤ Department of Computing …
Fitted Q-iteration in continuous action-space MDPs . … 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 theoretical analysis of this algorithm, proving what we believe is the …
Fitted Q-iteration in continuous action-space MDPs . … 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 theoretical analysis of this algorithm, proving what we believe is the …
