Add to ORKGWe 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
International audienceWe consider the problem of finding a near-optimal policy using value-function ...
Recently, fitted Q-iteration (FQI) based methods have become more popular due to their increased sa...
We consider batch reinforcement learning problems in continuous space, expected total discounted-rew...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
International audienceWe consider continuous state, continuous action batch reinforcement learning w...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
International audienceWe consider continuous state, continuous action batch reinforcement learning w...
ADPRL 2007. Honolulu, Hawaii, Apr 1-5, 2007. We consider batch reinforcement learning problems in c...
International audienceWe consider batch reinforcement learning problems in continuous space,expected...
International audienceWe consider batch reinforcement learning problems in continuous space,expected...
In the field of Reinforcement Learning, Markov Decision Processes with a finite number of states and...
Abstract — We consider batch reinforcement learning problems in continuous space, expected total dis...
Abstract As an important approach to solving complex sequential decision problems, reinforcement lea...
International audienceWe consider the problem of finding a near-optimal policy using value-function ...
Recently, fitted Q-iteration (FQI) based methods have become more popular due to their increased sa...
We consider batch reinforcement learning problems in continuous space, expected total discounted-rew...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
International audienceWe consider continuous state, continuous action batch reinforcement learning w...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
We consider continuous state, continuous action batch reinforcement learning where the goal is to le...
International audienceWe consider continuous state, continuous action batch reinforcement learning w...
ADPRL 2007. Honolulu, Hawaii, Apr 1-5, 2007. We consider batch reinforcement learning problems in c...
International audienceWe consider batch reinforcement learning problems in continuous space,expected...
International audienceWe consider batch reinforcement learning problems in continuous space,expected...
In the field of Reinforcement Learning, Markov Decision Processes with a finite number of states and...
Abstract — We consider batch reinforcement learning problems in continuous space, expected total dis...
Abstract As an important approach to solving complex sequential decision problems, reinforcement lea...
International audienceWe consider the problem of finding a near-optimal policy using value-function ...
Recently, fitted Q-iteration (FQI) based methods have become more popular due to their increased sa...
We consider batch reinforcement learning problems in continuous space, expected total discounted-rew...