Add to ORKGBayesian reinforcement learning (BRL) provides a formal framework for optimal exploration-exploitation tradeoff in reinforcement learning. Unfortunately, it is generally intractable to find the Bayes-optimal behavior except for restricted cases. As a consequence, many BRL algorithms, model-based approaches in particular, rely on approximated models or real-time search methods. In this paper, we present potential-based shaping for improving the learning performance in model-based BRL. We propose a number of potential functions that are particularly well suited for BRL, and are domain-independent in the sense that they do not require any prior knowledge about the actual environment. By incorporating the potential function into rea...
Model-based Bayesian Reinforcement Learn-ing (BRL) allows a sound formalization of the problem of ac...
Bayesian planning is a formally elegant approach to learning optimal behaviour under model uncertain...
Bayesian planning is a formally elegant approach to learning optimal behaviour under model uncertain...
Bayesian reinforcement learning (BRL) provides a principled framework for optimal exploration-exploi...
Reinforcement Learning has emerged as a useful framework for learning to perform a task optimally fr...
Abstract. In many Reinforcement Learning (RL) domains there is a high cost for generating experience...
Recently, Bayesian Optimization (BO) has been used to successfully optimize parametric policies in s...
Bayesian model-based reinforcement learning is a formally elegant approach to learning optimal behav...
Bayesian model-based reinforcement learning is a formally elegant approach to learning optimal behav...
Model-based Bayesian Reinforcement Learning (BRL) provides a principled solution to dealing with the...
Effectively leveraging model structure in reinforcement learning is a difficult task, but failure to...
Thesis (Ph.D.)--University of Washington, 2020Informed and robust decision making in the face of unc...
In the Bayesian Reinforcement Learning (BRL) setting, agents try to maximise the col- lected rewards...
The explore–exploit dilemma is one of the central challenges in Reinforcement Learn-ing (RL). Bayesi...
We address the problem of Bayesian reinforcement learning using efficient model-based online plannin...
Model-based Bayesian Reinforcement Learn-ing (BRL) allows a sound formalization of the problem of ac...
Bayesian planning is a formally elegant approach to learning optimal behaviour under model uncertain...
Bayesian planning is a formally elegant approach to learning optimal behaviour under model uncertain...
Bayesian reinforcement learning (BRL) provides a principled framework for optimal exploration-exploi...
Reinforcement Learning has emerged as a useful framework for learning to perform a task optimally fr...
Abstract. In many Reinforcement Learning (RL) domains there is a high cost for generating experience...
Recently, Bayesian Optimization (BO) has been used to successfully optimize parametric policies in s...
Bayesian model-based reinforcement learning is a formally elegant approach to learning optimal behav...
Bayesian model-based reinforcement learning is a formally elegant approach to learning optimal behav...
Model-based Bayesian Reinforcement Learning (BRL) provides a principled solution to dealing with the...
Effectively leveraging model structure in reinforcement learning is a difficult task, but failure to...
Thesis (Ph.D.)--University of Washington, 2020Informed and robust decision making in the face of unc...
In the Bayesian Reinforcement Learning (BRL) setting, agents try to maximise the col- lected rewards...
The explore–exploit dilemma is one of the central challenges in Reinforcement Learn-ing (RL). Bayesi...
We address the problem of Bayesian reinforcement learning using efficient model-based online plannin...
Model-based Bayesian Reinforcement Learn-ing (BRL) allows a sound formalization of the problem of ac...
Bayesian planning is a formally elegant approach to learning optimal behaviour under model uncertain...
Bayesian planning is a formally elegant approach to learning optimal behaviour under model uncertain...