Add to ORKGThis paper introduces a set of algorithms for Monte-Carlo Bayesian reinforcement learning. Firstly, Monte-Carlo estimation of upper bounds on the Bayes-optimal value function is employed to construct an optimistic policy. Secondly, gradient-based algorithms for approximate upper and lower bounds are introduced. Finally, we introduce a new class of gradient algorithms for Bayesian Bellman error minimisation. We theoretically show that the gradient methods are sound. Experimentally, we demonstrate the superiority of the upper bound method in terms of reward obtained. However, we also show that the Bayesian Bellman error method is a close second, despite its significant computational simplicity
International audiencePolicy gradient methods are reinforcement learning algorithms that adapt a par...
This thesis explores Bayesian and variational inference in the context of solving the reinforcement ...
We consider a general reinforcement learning problem and show that carefully combining the Bayesian...
This paper introduces a set of algorithms for Monte-Carlo Bayesian reinforcement learning. Firstly, ...
While the Bayesian decision-theoretic framework offers an elegantsolution to the problem of decision...
International audienceThis chapter surveys recent lines of work that use Bayesian techniques for rei...
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...
This dissertation considers a particular aspect of sequential decision making under uncertainty in w...
Bayesian reinforcement learning (RL) offers a principled and elegant approach for sequential decisio...
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...
Markov Decision Processes are a mathematical framework widely used for stochastic optimization and c...
Bayesian reinforcement learning (BRL) provides a principled framework for optimal exploration-exploi...
Reinforcement learning problems are often phrased in terms of Markov decision processes (MDPs)....
International audiencePolicy gradient methods are reinforcement learning algorithms that adapt a par...
This thesis explores Bayesian and variational inference in the context of solving the reinforcement ...
We consider a general reinforcement learning problem and show that carefully combining the Bayesian...
This paper introduces a set of algorithms for Monte-Carlo Bayesian reinforcement learning. Firstly, ...
While the Bayesian decision-theoretic framework offers an elegantsolution to the problem of decision...
International audienceThis chapter surveys recent lines of work that use Bayesian techniques for rei...
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...
This dissertation considers a particular aspect of sequential decision making under uncertainty in w...
Bayesian reinforcement learning (RL) offers a principled and elegant approach for sequential decisio...
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...
Markov Decision Processes are a mathematical framework widely used for stochastic optimization and c...
Bayesian reinforcement learning (BRL) provides a principled framework for optimal exploration-exploi...
Reinforcement learning problems are often phrased in terms of Markov decision processes (MDPs)....
International audiencePolicy gradient methods are reinforcement learning algorithms that adapt a par...
This thesis explores Bayesian and variational inference in the context of solving the reinforcement ...
We consider a general reinforcement learning problem and show that carefully combining the Bayesian...