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Found 4 papers, 0 papers with code
A Policy Gradient Algorithm for the Risk-Sensitive Exponential Cost MDP
We study the risk-sensitive exponential cost MDP formulation and develop a trajectory-based gradient algorithm to find the stationary point of the cost associated with a set of parameterized policies.
Adaptive KL-UCB based Bandit Algorithms for Markovian and i.i.d. Settings
rewards are a special case of Markov rewards and it is difficult to design an algorithm that works well independent of whether the underlying model is truly Markovian or i. i. d.
Online Reinforcement Learning of Optimal Threshold Policies for Markov Decision Processes
To overcome the curses of dimensionality and modeling of Dynamic Programming (DP) methods to solve Markov Decision Process (MDP) problems, Reinforcement Learning (RL) methods are adopted in practice.
A Structure-aware Online Learning Algorithm for Markov Decision Processes
In this paper, we propose a new RL algorithm which utilizes the known threshold structure of the optimal policy while learning by reducing the feasible policy space.
