Add to ORKGWe present an actor-critic scheme for reinforcement learning in complex domains. The main contribution is to show that planning and I/O dynamics can be separated such that an intractable planning problem reduces to a simple multi-armed bandit problem, where each lever stands for a potentially arbitrarily complex policy. Furthermore, we use the Bayesian control rule to construct an adaptive bandit player that is universal with respect to a given class of optimal bandit players, thus indirectly constructing an adaptive agent that is universal with respect to a given class of policies. © 2011 Springer-Verlag Berlin Heidelberg
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
We present an actor-critic scheme for reinforcement learning in complex domains. The main contributi...
We present an actor-critic scheme for reinforcement learning in complex domains. The main contributi...
Reinforcement learning (RL) is generally considered as the machine learning answer to the optimal co...
Thesis (Ph.D.)--University of Washington, 2020Informed and robust decision making in the face of unc...
Reinforcement learning refers to a machine learning paradigm in which an agent interacts with the en...
Abstract: Reinforcement learning (RL) is a kind of machine learning. It aims to optimize agents ’ po...
Recent advances in Bayesian reinforcement learn-ing (BRL) have shown that Bayes-optimality is theore...
An increasing number of complex problems have naturally posed significant challenges in decision-mak...
Actor-critic (AC) methods were among the earliest to be investigated in reinforcement learning (RL)....
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
This thesis explores Bayesian and variational inference in the context of solving the reinforcement ...
Reinforcement Learning has emerged as a useful framework for learning to perform a task optimally fr...
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
We present an actor-critic scheme for reinforcement learning in complex domains. The main contributi...
We present an actor-critic scheme for reinforcement learning in complex domains. The main contributi...
Reinforcement learning (RL) is generally considered as the machine learning answer to the optimal co...
Thesis (Ph.D.)--University of Washington, 2020Informed and robust decision making in the face of unc...
Reinforcement learning refers to a machine learning paradigm in which an agent interacts with the en...
Abstract: Reinforcement learning (RL) is a kind of machine learning. It aims to optimize agents ’ po...
Recent advances in Bayesian reinforcement learn-ing (BRL) have shown that Bayes-optimality is theore...
An increasing number of complex problems have naturally posed significant challenges in decision-mak...
Actor-critic (AC) methods were among the earliest to be investigated in reinforcement learning (RL)....
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
This thesis explores Bayesian and variational inference in the context of solving the reinforcement ...
Reinforcement Learning has emerged as a useful framework for learning to perform a task optimally fr...
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...
Learning in real-world domains often requires to deal with continuous state and action spaces. Alth...