Add to ORKGMarkov Decision Processes are a mathematical framework widely used for stochastic optimization and control problems. Reinforcement Learning is a branch of Artificial Intelligence that deals with stochastic environments where the dynamics of the system are unknown. A major issue for learning algorithms is the need to balance the amount of exploration of new experiences with the exploitation of existing knowledge. We present three methods for dealing with this exploration-exploitation tradeoff for Markov Decision Processes. The approach taken is Bayesian, in that we use and maintain a model estimate. The existence of an optimal policy for Bayesian exploration has been shown, but its computation is infeasible. We present three approximatio...
This paper addresses the problem of decision making in unknown finite Markov Decision Processes (MDP...
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...
This dissertation considers a particular aspect of sequential decision making under uncertainty in w...
In recent work, Bayesian methods for exploration in Markov decision processes (MDPs) and for solving...
Increasing attention has been paid to reinforcement learning algorithms in recent years, partly due ...
We consider the inverse reinforcement learning problem, that is, the problem of learning from, and t...
This work addresses the problem of estimating the optimal value function in a Markov Decision Proces...
In this work we consider probabilistic approaches to sequential decision making. The ultimate goal i...
This thesis addresses the dilemma between exploration and exploitation as it is faced by reinforceme...
We consider the problem of learning a policy for a Markov decision process consistent with data capt...
Bayesian planning is a formally elegant approach to learning optimal behaviour under model uncertain...
Policy search algorithms have facilitated application of Reinforcement Learning (RL) to dynamic syst...
A probabilistic reinforcement learning algorithm is presented for finding control policies in contin...
Bayesian planning is a formally elegant approach to learning optimal behaviour under model uncertain...
This paper addresses the problem of decision making in unknown finite Markov Decision Processes (MDP...
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...
This dissertation considers a particular aspect of sequential decision making under uncertainty in w...
In recent work, Bayesian methods for exploration in Markov decision processes (MDPs) and for solving...
Increasing attention has been paid to reinforcement learning algorithms in recent years, partly due ...
We consider the inverse reinforcement learning problem, that is, the problem of learning from, and t...
This work addresses the problem of estimating the optimal value function in a Markov Decision Proces...
In this work we consider probabilistic approaches to sequential decision making. The ultimate goal i...
This thesis addresses the dilemma between exploration and exploitation as it is faced by reinforceme...
We consider the problem of learning a policy for a Markov decision process consistent with data capt...
Bayesian planning is a formally elegant approach to learning optimal behaviour under model uncertain...
Policy search algorithms have facilitated application of Reinforcement Learning (RL) to dynamic syst...
A probabilistic reinforcement learning algorithm is presented for finding control policies in contin...
Bayesian planning is a formally elegant approach to learning optimal behaviour under model uncertain...
This paper addresses the problem of decision making in unknown finite Markov Decision Processes (MDP...
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...