Add to ORKGModel-based Bayesian Reinforcement Learning (BRL) provides a principled solution to dealing with the exploration-exploitation trade-off, but such methods typically assume a fully observable environments. The few Bayesian RL methods that are applicable in partially observable domains, such as the Bayes-Adaptive POMDP (BA-POMDP), scale poorly. To address this issue, we introduce the Factored BA-POMDP model (FBA-POMDP), a framework that is able to learn a compact model of the dynamics by exploiting the underlying structure of a POMDP. The FBA-POMDP framework casts the problem as a planning task, for which we adapt the Monte-Carlo Tree Search planning algorithm and develop a belief tracking method to approximate the joint posterior over the sta...
In recent work, Bayesian methods for exploration in Markov decision processes (MDPs) and for solving...
Bayesian methods for reinforcement learning (BRL) allow model uncertainty to be considered explicitl...
For autonomous robots, we propose an approximate model-based Bayesian reinforcement learning (MB-BRL...
Bayesian Reinforcement Learning has generated substantial interest recently, as it provides an elega...
Reinforcement Learning has emerged as a useful framework for learning to perform a task optimally fr...
While reinforcement learning (RL) has made great advances in scalability, exploration and partial ob...
Effectively leveraging model structure in reinforcement learning is a difficult task, but failure to...
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...
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 formal framework for optimal exploration-exploitat...
Abstract. In many Reinforcement Learning (RL) domains there is a high cost for generating experience...
The POMDP is a powerful framework for reasoning under outcome and information uncertainty, but const...
In recent work, Bayesian methods for exploration in Markov decision processes (MDPs) and for solving...
In recent work, Bayesian methods for exploration in Markov decision processes (MDPs) and for solving...
Bayesian methods for reinforcement learning (BRL) allow model uncertainty to be considered explicitl...
For autonomous robots, we propose an approximate model-based Bayesian reinforcement learning (MB-BRL...
Bayesian Reinforcement Learning has generated substantial interest recently, as it provides an elega...
Reinforcement Learning has emerged as a useful framework for learning to perform a task optimally fr...
While reinforcement learning (RL) has made great advances in scalability, exploration and partial ob...
Effectively leveraging model structure in reinforcement learning is a difficult task, but failure to...
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...
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 formal framework for optimal exploration-exploitat...
Abstract. In many Reinforcement Learning (RL) domains there is a high cost for generating experience...
The POMDP is a powerful framework for reasoning under outcome and information uncertainty, but const...
In recent work, Bayesian methods for exploration in Markov decision processes (MDPs) and for solving...
In recent work, Bayesian methods for exploration in Markov decision processes (MDPs) and for solving...
Bayesian methods for reinforcement learning (BRL) allow model uncertainty to be considered explicitl...
For autonomous robots, we propose an approximate model-based Bayesian reinforcement learning (MB-BRL...