Add to ORKGBayesian model-based reinforcement learning is a formally elegant approach to learning optimal behaviour under model uncertainty, trading off exploration and exploitation in an ideal way. Unfortunately, finding the resulting Bayes-optimal policies is notoriously taxing, since the search space becomes enormous. In this paper we introduce a tractable, sample-based method for approximate Bayes-optimal planning which exploits Monte-Carlo tree search. Our approach outperformed prior Bayesian model-based RL algorithms by a significant margin on several well-known benchmark problems -- because it avoids expensive applications of Bayes rule within the search tree by lazily sampling models from the current beliefs. We illustrate the advantages of ou...
Model-based Bayesian Reinforcement Learning (BRL) provides a principled solution to dealing with the...
Bayesian reinforcement learning (BRL) provides a formal framework for optimal exploration-exploitat...
Bayesian reinforcement learning (BRL) provides a principled framework for optimal exploration-exploi...
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...
We address the problem of Bayesian reinforcement learning using efficient model-based online plannin...
For autonomous robots, we propose an approximate model-based Bayesian reinforcement learning (MB-BRL...
Only rich and sophisticated statistical models are adequate for agents that must learn to navi- gate...
Research in reinforcement learning has produced algo-rithms for optimal decision making under uncert...
Research in reinforcement learning has produced algorithms for optimal decision making under uncerta...
Reinforcement Learning has emerged as a useful framework for learning to perform a task optimally fr...
Online solvers for partially observable Markov decision processes have difficulty scaling to problem...
It is often assumed that agents in multiagent systems with state uncertainty have full knowledge of ...
It is often assumed that agents in multiagent systems with state uncertainty have full knowledge of ...
Model-based Bayesian Reinforcement Learning (BRL) provides a principled solution to dealing with the...
Bayesian reinforcement learning (BRL) provides a formal framework for optimal exploration-exploitat...
Bayesian reinforcement learning (BRL) provides a principled framework for optimal exploration-exploi...
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...
We address the problem of Bayesian reinforcement learning using efficient model-based online plannin...
For autonomous robots, we propose an approximate model-based Bayesian reinforcement learning (MB-BRL...
Only rich and sophisticated statistical models are adequate for agents that must learn to navi- gate...
Research in reinforcement learning has produced algo-rithms for optimal decision making under uncert...
Research in reinforcement learning has produced algorithms for optimal decision making under uncerta...
Reinforcement Learning has emerged as a useful framework for learning to perform a task optimally fr...
Online solvers for partially observable Markov decision processes have difficulty scaling to problem...
It is often assumed that agents in multiagent systems with state uncertainty have full knowledge of ...
It is often assumed that agents in multiagent systems with state uncertainty have full knowledge of ...
Model-based Bayesian Reinforcement Learning (BRL) provides a principled solution to dealing with the...
Bayesian reinforcement learning (BRL) provides a formal framework for optimal exploration-exploitat...
Bayesian reinforcement learning (BRL) provides a principled framework for optimal exploration-exploi...