Add to ORKGInternational audienceWe 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
We consider the problem of model-free reinforcement learning in the Markovian decision processes (MD...
We consider the problem of model-free reinforcement learning in the Markovian decision processes (MD...
In the field of Reinforcement Learning, Markov Decision Processes with a finite number of states and...
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...
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...
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...
We consider the problem of model-free reinforcement learning in the Markovian decision processes (MD...
We consider the problem of model-free reinforcement learning in the Markovian decision processes (MD...
We consider the problem of model-free reinforcement learning in the Markovian decision processes (MD...
We consider the problem of model-free reinforcement learning in the Markovian decision processes (MD...
We consider the problem of model-free reinforcement learning in the Markovian decision processes (MD...
In the field of Reinforcement Learning, Markov Decision Processes with a finite number of states and...
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...
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...
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...
We consider the problem of model-free reinforcement learning in the Markovian decision processes (MD...
We consider the problem of model-free reinforcement learning in the Markovian decision processes (MD...
We consider the problem of model-free reinforcement learning in the Markovian decision processes (MD...
We consider the problem of model-free reinforcement learning in the Markovian decision processes (MD...
We consider the problem of model-free reinforcement learning in the Markovian decision processes (MD...
In the field of Reinforcement Learning, Markov Decision Processes with a finite number of states and...